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: "Incremental Pruning: A Simple, Fast, Exact Algorithm for Partially Observable Markov Decision Processes"
  Abstract: Most exact algorithms for general pomdps use a form of dynamic programming in which a piecewise-linear and convex representation of one value function is transformed into another. We examine variations of the "incremental pruning" approach for solving this problem and compare them to earlier algorithms from theoretical and empirical perspectives. We find that incremental pruning is presently the most efficient algorithm for solving pomdps.

Connected through hyperedge HE-1 (induced by source paper "Planning Medical Therapy Using Partially Observable Markov Decision Processes."):
  with members:
    (none)

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