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 "Markov Decision Processes in Large State Spaces"):
  Abstract: In this paper we propose a new framework for studying Markov decision processes (MDPs), based on ideas from statistical mechanics. The goal of learning in MDPs is to find a policy that yields the maximum expected return over time. In choosing policies, agents must therefore weigh the prospects of short-term versus long-term gains. We study a simple MDP in which the agent must constantly decide between exploratory jumps and local reward mining in state space. The number of policies to choose from grows exponentially with the size of the state space, N . We view the expected returns as defining an energy landscape over policy space. Methods from statistical mechanics are used to analyze this landscape in the thermodynamic limit N ! 1. We calculate the overall distribution of expected returns, as well as the distribution of returns for policies at a fixed Hamming distance from the optimal one. We briefly discuss the problem of learning optimal policies from empirical estimates of the expected return. As a first step, we relate our findings for the entropy to the limit of high-temperature learning. Numerical simulations support the theoretical results.

Includes members:
  Member "Learning to Act using Real-Time Dynamic Programming":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "On the Complexity of Solving Markov Decision Problems")
  Member "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "TDLeaf(): Combining Temporal Difference learning with game-tree search.")
  Member "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Acting under Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation")
  Member "Rigorous Learning Curve Bounds from Statistical Mechanics":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with")
  Member "Learning Curve Bounds for Markov Decision Processes with Undiscounted Rewards":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Learning to Act using Real-Time Dynamic Programming")

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