human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent groups of papers sharing a common citing paper.

Details of the hypergraph:
Center node: "Associative Reinforcement Learning: A Generate and Test Algorithm"
  Abstract: An agent that must learn to act in the world by trial and error faces the reinforcement learning problem, which is quite different from standard concept learning. Although good algorithms exist for this problem in the general case, they are often quite inefficient and do not exhibit generalization. One strategy is to find restricted classes of action policies that can be learned more efficiently. This paper pursues that strategy by developing an algorithm that performans an on-line search through the space of action mappings, expressed as Boolean formulae. The algorithm is compared with existing methods in empirical trials and is shown to have very good performance.

Connected through hyperedge HE-1 (induced by source paper "Associative Reinforcement Learning: Functions in k-DNF"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Expected Mistake Bound Model for On-Line Reinforcement Learning"
    - "Exploratory Learning in the Game of GO"

Question: Please tell me which class the center node belongs to. The 7 classes are: Neural_Networks, Reinforcement_Learning, Probabilistic_Methods, Theory, Genetic_Algorithms, Case_Based, Rule_Learning. Directly output the class name.

gpt: Reinforcement_Learning