Based on the given feature n_r_ecg_p_08, which represents "Paroxysms of supraventricular tachycardia on ECG at the time of admission to hospital", we need to analyze its relationship with the target variable "chronic heart failure".

To determine this relationship, we would ideally analyze a dataset where both the feature and target variables are available. By analyzing this data, we can identify patterns and understand whether certain values of n_r_ecg_p_08 are associated with chronic heart failure or not.

Unfortunately, we do not have access to such data in this context, which limits our ability to analyze the relationship directly. However, we can make use of prior knowledge and assumptions to provide some insights.

Based on general medical knowledge, it is possible that patients with paroxysms of supraventricular tachycardia on ECG at the time of admission to the hospital may have an increased risk of chronic heart failure. Supraventricular tachycardia involves rapid heart rates originating above the ventricles, and if it occurs frequently or persists, it may lead to heart damage and subsequently chronic heart failure.

Given this assumption, we can create a dictionary in the requested format while considering the possible relationship between the feature and the target. However, since we do not have specific data to validate this relationship, we cannot provide an accurate list of values for each target class.

Here is an example of the requested dictionary:

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

In this case, we assume that the feature n_r_ecg_p_08 values are either "no" or "yes". However, please note that without actual data, there is a possibility of missing or misjudging certain values that might be hard to predict accurately.

Keep in mind that this analysis is based on assumptions and general medical knowledge. For a more accurate analysis, it is necessary to have access to real patient data with labeled target variables to determine the relationship between the feature and the target more precisely.