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: "Efficient dynamic-programming updates in partially observable Markov decision processes"
  Abstract: We examine the problem of performing exact dynamic-programming updates in partially observable Markov decision processes (pomdps) from a computational complexity viewpoint. Dynamic-programming updates are a crucial operation in a wide range of pomdp solution methods and we find that it is intractable to perform these updates on piecewise-linear convex value functions for general pomdps. We offer a new algorithm, called the witness algorithm, which can compute updated value functions efficiently on a restricted class of pomdps in which the number of linear facets is not too great. We compare the witness algorithm to existing algorithms analytically and empirically and find that it is the fastest algorithm over a wide range of pomdp sizes.

Connected through hyperedge HE-1 (induced by source paper "Approximating Optimal Policies for Partially Observable Stochastic Domains"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Learning policies for partially observable environments: Scaling up"
    - "Acting under Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation"
    - "Incremental methods for computing bounds in partially observable Markov decision processes"
    - "Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems"
    - "Rationality and Intelligence"
    - "Adaptive probabilistic networks"

Connected through hyperedge HE-2 (induced by source paper "Incremental methods for computing bounds in partially observable Markov decision processes"):
  with members:
    - "Learning policies for partially observable environments: Scaling up"
    - "Approximating Optimal Policies for Partially Observable Stochastic Domains"

Connected through hyperedge HE-3 (induced by source paper "Learning policies for partially observable environments: Scaling up"):
  with members:
    - "Some Experiments with Real-time Decision Algorithms"
    - "A Formal Framework for Speedup Learning from Problems and Solutions"
    - "Acting under Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation"
    - "Approximating Optimal Policies for Partially Observable Stochastic Domains"
    - "Learning sorting and decision trees with POMDPs"
    - "Incremental methods for computing bounds in partially observable Markov decision processes"

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