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 Methods for Continuous-Time Markov Decision Problems"):
  Abstract: Semi-Markov Decision Problems are continuous time generalizations of discrete time Markov Decision Problems. A number of reinforcement learning algorithms have been developed recently for the solution of Markov Decision Problems, based on the ideas of asynchronous dynamic programming and stochastic approximation. Among these are TD(), Q-learning, and Real-time Dynamic Programming. After reviewing semi-Markov Decision Problems and Bellman's optimality equation in that context, we propose algorithms similar to those named above, adapted to the solution of semi-Markov Decision Problems. We demonstrate these algorithms by applying them to the problem of determining the optimal control for a simple queueing system. We conclude with a discussion of circumstances under which these algorithms may be usefully ap plied.

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 "In  Improving Elevator Performance Using Reinforcement Learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Spurious Solutions to the Bellman Equation")
  Member "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "TDLeaf(): Combining Temporal Difference learning with game-tree search.")
  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 "Self-Improving Factory Simulation using Continuous-time Average-Reward Reinforcement Learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Value Function Based Production Scheduling")

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