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 methods for computing bounds in partially observable Markov decision processes"
  Abstract: Partially observable Markov decision processes (POMDPs) allow one to model complex dynamic decision or control problems that include both action outcome uncertainty and imperfect observabil-ity. The control problem is formulated as a dynamic optimization problem with a value function combining costs or rewards from multiple steps. In this paper we propose, analyse and test various incremental methods for computing bounds on the value function for control problems with infinite discounted horizon criteria. The methods described and tested include novel incremental versions of grid-based linear interpolation method and simple lower bound method with Sondik's updates. Both of these can work with arbitrary points of the belief space and can be enhanced by various heuristic point selection strategies. Also introduced is a new method for computing an initial upper bound the fast informed bound method. This method is able to improve significantly on the standard and commonly used upper bound computed by the MDP-based method. The quality of resulting bounds are tested on a maze navigation problem with 20 states, 6 actions and 8 observations.

Connected through hyperedge HE-1 (induced by source paper "Efficient dynamic-programming updates 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-2 (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"
    - "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-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"
    - "Efficient dynamic-programming updates 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