human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Learning policies for partially observable environments: Scaling up":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Acting under Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation")
  Member "Approximating Optimal Policies for Partially Observable Stochastic Domains":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Acting under Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation")
  Member "Efficient dynamic-programming updates in partially observable Markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Approximating Optimal Policies for Partially Observable Stochastic Domains")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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