Based on the feature description and task, we want to analyze how the respondent's gender (RIAGENDR) relates to their age group (target variable). From prior knowledge, we can assume that certain age ranges correspond to "Adult" and "Senior" age groups. 

For this analysis, let's assume that age >= 65 represents the "Senior" age group, and age < 65 represents the "Adult" age group.

Based on this assumption, we can create a dictionary with the following format:

```json
{
    "Adult": [1.0, 1.0, 1.0, 2.0, 2.0],
    "Senior": [2.0, 2.0, 1.0, 1.0, 1.0] 
}
```

In this dictionary, the "Adult" key represents the target class "Adult" and the "Senior" key represents the target class "Senior". The values associated with each class are a list of RIAGENDR feature values that are typical for that age group.

For example, in the "Adult" age group, the RIAGENDR values could be 1.0 or 2.0. Similarly, in the "Senior" age group, the RIAGENDR values could also be 1.0 or 2.0.

Please note that these values are just examples and may not accurately represent the data. To create a more accurate dictionary, you would need to analyze the actual data and determine the typical RIAGENDR values for each age group.