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 "Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems"):
  Abstract: Increasing attention has been paid to reinforcement learning algorithms in recent years, partly due to successes in the theoretical analysis of their behavior in Markov environments. If the Markov assumption is removed, however, neither generally the algorithms nor the analyses continue to be usable. We propose and analyze a new learning algorithm to solve a certain class of non-Markov decision problems. Our algorithm applies to problems in which the environment is Markov, but the learner has restricted access to state information. The algorithm involves a Monte-Carlo policy evaluation combined with a policy improvement method that is similar to that of Markov decision problems and is guaranteed to converge to a local maximum. The algorithm operates in the space of stochastic policies, a space which can yield a policy that performs considerably better than any deterministic policy. Although the space of stochastic policies is continuous|even for a discrete action space|our algorithm is computationally tractable.

Includes members:
  Member "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "TDLeaf(): Combining Temporal Difference learning with game-tree search.")
  Member "Active Gesture Recognition using Partially Observable Markov Decision Processes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Planning and Acting in Partially Observable Stochastic Domains")
  Member "Approximating Optimal Policies for Partially Observable Stochastic Domains":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Acting under Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation")
  Member "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Towards a Reactive Critic")
  Member "Learning Without State-Estimation in Partially Observable Markovian Decision Processes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Reinforcement Learning with Soft State Aggregation")

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