Based on prior knowledge, the feature "default" refers to whether the client has credit in default or not. This is a categorical variable with two possible values: "no" and "yes". 

To analyze the relationship between this feature and the task of whether the client subscribes to a term deposit or not, we can examine the distribution of the feature values for each target class. 

Let's assume that the dataset consists of multiple instances where a client either subscribes to a term deposit ("yes" target class) or does not subscribe ("no" target class). By analyzing the dataset, we can observe the following:

For the target class "no" (client does not subscribe to a term deposit):
- There are instances where the value of the "default" feature is "no".
- There are instances where the value of the "default" feature is "yes".

For the target class "yes" (client subscribes to a term deposit):
- There are instances where the value of the "default" feature is "no".
- There are instances where the value of the "default" feature is "yes".

Based on the above analysis, the dictionary representing the relationship between the "default" feature and the target class is as follows:

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

Please note that this analysis assumes that both categories "no" and "yes" are present for both target classes. If there are instances where a specific category of the "default" feature is not present for a target class, it should not be included in the respective list.