Based on my prior knowledge, a Type 1 Second-degree AV block (Mobitz I/Wenckebach) on an ECG at the time of admission to the hospital can be an indication of chronic heart failure. Therefore, it is likely that the presence of this condition (yes category) in the n_p_ecg_p_04 feature would show chronic heart failure (the target class).

To analyze the relationship between the feature and the target class, we need to gather information on the distribution of the n_p_ecg_p_04 feature for each target class. 

Here is the analysis:

For the target class "no" (no chronic heart failure):
- The feature value "no" (no Type 1 Second-degree AV block) may occur frequently, indicating that for patients without chronic heart failure, it is common to not have this condition at the time of admission.

For the target class "yes" (chronic heart failure):
- The feature value "yes" (presence of Type 1 Second-degree AV block) may occur more frequently compared to the "no" target class. This would suggest that for patients with chronic heart failure, there is a higher probability of having this condition.

Based on this analysis, the dictionary would look like this:

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

Note: In this case, the n_p_ecg_p_04 feature has only two categories ("no" and "yes"). If there were more categories, we would include all possible values in the corresponding target class list as long as they are observed in the data.