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: "Learning policies for partially observable environments: Scaling up"
  Abstract: Partially observable Markov decision processes (pomdp's) model decision problems in which an agent tries to maximize its reward in the face of limited and/or noisy sensor feedback. While the study of pomdp's is motivated by a need to address realistic problems, existing techniques for finding optimal behavior do not appear to scale well and have been unable to find satisfactory policies for problems with more than a dozen states. After a brief review of pomdp's, this paper discusses several simple solution methods and shows that all are capable of finding near-optimal policies for a selection of extremely small pomdp's taken from the learning literature. In contrast, we show that none are able to solve a slightly larger and noisier problem based on robot navigation. We find that a combination of two novel approaches performs well on these problems and suggest methods for scaling to even larger and more complicated domains.

Connected through hyperedge HE-1 (induced by source paper "Acting under Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation"):
  with members:
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "Approximating Optimal Policies for Partially Observable Stochastic Domains"
    - "Learning sorting and decision trees with POMDPs"

Connected through hyperedge HE-2 (induced by source paper "A Formal Framework for Speedup Learning from Problems and Solutions"):
  with members:
    - "A Statistical Approach to Solving the EBL Utility Problem"

Connected through hyperedge HE-3 (induced by source paper "Efficient dynamic-programming updates in partially observable Markov decision processes"):
  with members:
    - "Approximating Optimal Policies for Partially Observable Stochastic Domains"
    - "Incremental methods for computing bounds in partially observable Markov decision processes"

Connected through hyperedge HE-4 (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,"
    - "Acting under Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation"
    - "Incremental methods for computing bounds in partially observable Markov decision processes"
    - "Efficient dynamic-programming updates 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-5 (induced by source paper "Learning sorting and decision trees with POMDPs"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "Acting under Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation"

Connected through hyperedge HE-6 (induced by source paper "Incremental methods for computing bounds in partially observable Markov decision processes"):
  with members:
    - "Approximating Optimal Policies for Partially Observable Stochastic Domains"
    - "Efficient dynamic-programming updates in partially observable Markov decision processes"

Connected through hyperedge HE-7 (induced by source paper "Some Experiments with Real-time Decision Algorithms"):
  with members:
    - "Efficient Inference in Bayes Networks As A Combinatorial Optimization Problem"

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