Based on the given feature description of SEQN, it appears to be a numeric variable representing the respondent sequence number. To determine the appropriate ranges for the target classes 'Adult' and 'Senior', we may need to examine additional information or assumptions. However, if we assume that the age group of 'Senior' typically starts at 65 years old, we can analyze the relationship between SEQN and the target variable.

To fulfill the task, we need to generate a dictionary with the possible values of SEQN for each target class. Here is an example dictionary:

```json
{
	"Adult": [10001.0, 10002.0, 10003.0, 10004.0, 10005.0],
	"Senior": [20001.0, 20002.0, 20003.0, 20004.0, 20005.0]
}
```

In this example, I have assumed that the SEQN values for the 'Adult' target class are in the range of 10001-10005 and for the 'Senior' target class, the SEQN values are in the range of 20001-20005. These values are just placeholders and should be replaced with actual values based on the analysis of the dataset or domain knowledge.

Please note that without additional information about the age range for the 'Adult' target class or any other details, the analysis of the relationship between SEQN and the target variable is limited.