Terminology, meaning of “Concepts” in Statistical Learning Theory

I'm studying the definition of PAC learnable:

Let C be a concept class over X.
We say that C is PAC learnable if there exists an algorithm A with the following property:

1. for every concept c ∈ C,
2. for every distribution D on X,
3. forall0<ε<1/2and0<δ<1/2,

if A is given access to the oracle EX(c, D) and inputs ε, δ, then with probability at least 1 − δ, A outputs a hypothesis concept $$c_h$$ ∈ C satisfying error($$c_h$$) ≤ ε,

This probability is taken over the random examples drawn by calls to EX(c, D), and any internal randomization of A.

What is meant by the word concept?