Based on the feature description and task, we are trying to determine if the patient's myocardial infarction complications data shows chronic heart failure. The feature, GEPAR_S_n, represents the use of anticoagulants in the ICU and has two categories: 'yes' and 'no'.

To analyze the relationship between this feature and the task, we need prior knowledge or data that links the use of anticoagulants to chronic heart failure. Without this information, it is difficult to determine the exact relationship between the feature and the target variable.

However, to create the dictionary as requested, we can assume that the presence or absence of anticoagulants may affect the likelihood of chronic heart failure. We can provide a general analysis based on this assumption:

```json
{
    "no": ["no"],
    "yes": ["yes"]
}
```

In this analysis, the feature values for target class "no" are limited to 'no', indicating that there is a possibility that patients without anticoagulant use may not exhibit chronic heart failure. Similarly, for target class "yes", the feature values are limited to 'yes', suggesting that patients with anticoagulant use might be more likely to show chronic heart failure.

Please note that this analysis is based on assumptions and generalizations, and the actual relationship between the feature and the target variable could be more complex. To obtain a more accurate analysis, it would be beneficial to have domain-specific knowledge or access to relevant data on the relationship between anticoagulant use and chronic heart failure.