After a preliminary review of the contents of both files, I found that the content previewed doesn't directly indicate the issues based on the provided hint. Now, I will further search for specific mentions of "multi-class-classification" and the file sections describing the labels in both files. This way, we can identify if the hint about the discrepancy regarding the classification type and the number of labels actually holds, leading us to accurate issue descriptions.Based on the further analysis:

- The mention of "multi-class-classification" is not found in the README.md file but is present in the russian_super_glue.py file.
- There wasn't a specific section explicitly defining "labels" in the python file within the previewed and analyzed parts. Given this context, let's address the potential issue regarding the misclassification as multi-class when the dataset may only have two labels. For a more thorough investigation, we would typically need to see the complete definition of the dataset structure in the Python file or access to its contents.

Considering the hint and our findings, here's the identified issue:

```json
{
    "issue": "Misclassification as Multi-Class in Presence of Two Labels",
    "evidence": "task_ids:\n- natural-language-inference\n- multi-class-classification",
    "description": "The python file 'russian_super_glue.py' mentions 'multi-class-classification' under task_ids. However, based on the hint provided, there might only be two labels available in the dataset, which contradicts multi-class classification requirements. The term 'multi-class-classification' suggests more than two classes or labels should be available for it to be an appropriate classification, implying a misalignment with the dataset's actual structure or available labels."
}
```