Is Self-Supervised Learning a task of Representation Learning?

Maybe a weird question but: Currently, I'm writing a seminar paper about Self Supervised Learning for time series data. For this paper, I have to find methods to prepare unlabelled time series data with SSL techniques to perform a classification task. In a scientific paper, I was able to find time series representation learning methods. Another SSL paper used one of those methods to do the classification on a specific dataset.

Now I have to admit that I'm kind of confused about the relation between SSL and representation learning. On towardsdatascience I found the following definitions:

The motivation behind Self-supervised learning is to learn useful representations of the data from unlabelled pool of data using self-supervision first and then fine-tune the representations with few labels for the supervised downstream task.

Self-supervised learning is a representation learning method where a supervised task is created out of the unlabelled data.

So can I assume that representation learning is an approach of ML and one of its tasks is SSL?