What does the notation ${s'\sim T(s,a,\cdot)}$ mean?

I have been seeing notations on Expectations with their respective subscripts such as $$E_{s_0 \sim D}[V^\pi (s_0)] = \Sigma_{t=0}^\infty[\gamma^t\phi(s_t)]$$. This equation is taken from https://ai.stanford.edu/~ang/papers/icml04-apprentice.pdf and $$Q^\pi(s,a,R) = R(s) + \gamma E_{s'\sim T(s,a,\cdot)}[V^\pi(s',R)]$$ ,in the case of the Bayesian IRL paper.(https://www.aaai.org/Papers/IJCAI/2007/IJCAI07-416.pdf)

I understand that $$s_0 \sim D$$ means that the starting state $$s_0$$ is drawn from a distribution of starting states $$D$$. But how do we understand the latter with subscript $${s'\sim T(s,a,\cdot)}$$ ? How is $$s'$$ drawn from a distribution of transition probabilities?

The dot ($$.$$) at the end of $$T(s,a,.)$$ shows all possible states that we can go from state $$S$$ by doing action $$a$$. As you know there are some probabilities here for choosing those states, that the sum of these probabilities is equal to 1. Hence, $$T(s,a,.)$$ is a probability distribution.