Based on the given feature "contact" and the task of determining whether a client subscribes to a term deposit, we can analyze the relationship between the feature and the target variable by examining the distribution of "contact" for both the "yes" and "no" classes.

Analyzing the distribution of the "contact" feature can help us understand if there are any patterns or tendencies that might influence the client's decision to subscribe to a term deposit.

Here's the analysis and the resulting dictionary:

```json
{
	"no": ["unknown", "cellular", "telephone"],
	"yes": ["cellular", "telephone"]
}
```

In the "no" class, clients have been contacted through all three types of communication: 'unknown', 'cellular', and 'telephone'. This suggests that the type of contact does not have a strong influence on whether the client subscribes to a term deposit.

On the other hand, in the "yes" class, clients have been contacted primarily through 'cellular' and 'telephone'. This indicates that these two types of contact might be more effective in persuading clients to subscribe to a term deposit.

Note that the 'unknown' category is included in the "no" class since it represents a type of contact that did not result in subscription. However, since the question specifies that list of each target class should not be empty, we include it in the "no" class even though it is hard to predict its influence on the target variable.

Based on this analysis, we can conclude that 'cellular' and 'telephone' contacts might have a stronger relationship with clients subscribing to a term deposit compared to other types of contact.