add

src/about.py

@@ -76,13 +76,14 @@ Once you have developed your model and got results, you can submit your test res
 - **Your email**: Primary contact's email 
 - **RelBench version**: The RelBench version used to conduct the experiments. 
 - **Model name**: The name of the method. This is an unique identifier of the model. Please be distinct with any existing model names. It will be overriden if the same model name is submitted.
-- **Task name**: The name of an Relbench dataset that you use to evaluate the method. Choose from the dropdown menus.
 - **Is it an official submission**: Whether the implementation is official (implementation by authors who proposed the method) or unofficial (re-implementation of the method by non-authors).
-- **Test performance**: Raw test performance output by RelBench model evaluators, where average and unbiased standard deviation must be taken over 5 different random seeds. You can either not fix random seeds at all, or use the random seeds from 0 to 4. We highly discourage you to tune the random seeds.
-- **Validation performance**: Validation performance of the model that is used to report the test performance above.
 - **Paper URL Link**: The original paper describing the method (arXiv link is recommended. paper needs not be peer-reviewed). If your method has any original component (e.g., even just combining existing methods XXX and YYY), you have to write a technical report describing it (e.g., how you exactly combined XXX and YYY).
 - **GitHub URL Link**: The Github repository or directory containining all code to reproduce the result. A placeholder repository is not allowed.
+- **Task Track**: Choose the task you submit to, from node classification, node regression, and link prediction.
 - **Honor code**: Please acknowledge that your submission adheres to all the ethical policies and your result is reproducible.
+- **Test performance**: Raw test performance output by RelBench model evaluators, where average and unbiased standard deviation must be taken over 5 different random seeds. You can either not fix random seeds at all, or use the random seeds from 0 to 4. We highly discourage you to tune the random seeds.
+- **Validation performance**: Validation performance of the model that is used to report the test performance above.
+
 """
 
 CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"