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: "Exploratory Learning in the Game of GO"
  Abstract: This paper considers the importance of exploration to game-playing programs which learn by playing against opponents. The central question is whether a learning program should play the move which offers the best chance of winning the present game, or if it should play the move which has the best chance of providing useful information for future games. An approach to addressing this question is developed using probability theory, and then implemented in two different learning methods. Initial experiments in the game of Go suggest that a program which takes exploration into account can learn better against a knowledgeable opponent than a program which does not.

Connected through hyperedge HE-1 (induced by source paper "Exploration in Machine Learning"):
  with members:
    (none)

Connected through hyperedge HE-2 (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"
    - "Associative Reinforcement Learning: A Generate and Test Algorithm"
    - "Expected Mistake Bound Model for On-Line Reinforcement Learning"

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