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

Based on the given feature description, the feature "n_r_ecg_p_04" represents whether the patient had frequent premature ventricular contractions on ECG at the time of admission to the hospital. In order to determine the presence of chronic heart failure, we need to examine the relationship between this feature and the target variable.

From the given task, we are trying to predict whether the patient has chronic heart failure. The target variable is binary, with two possible values: "yes" and "no". 

Analyzing the possible values of the feature, we can see that there are two categories: "yes" and "no". In the context of chronic heart failure, it is reasonable to assume that if a patient has frequent premature ventricular contractions on ECG at the time of admission to the hospital (i.e., the value of the feature is "yes"), it increases the likelihood of having chronic heart failure (i.e., the target class is "yes"). Conversely, if the patient does not have frequent premature ventricular contractions on ECG at the time of admission (i.e., the value of the feature is "no"), it decreases the likelihood of having chronic heart failure (i.e., the target class is "no").

Hence, based on the analysis, the dictionary representation for the relationship would be as follows:
- For the target class "no" (indicating no chronic heart failure), the feature value "yes" is possible.
- For the target class "yes" (indicating chronic heart failure), the feature value "no" is possible.

Note: It is specified in the task that if there are other values of the feature that are hard to predict, they should not be included in the dictionary. In this case, since there are only two possible values of the feature ("yes" and "no"), we can exclude any other values from the dictionary.