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 "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms"):
  Abstract: Recent developments in the area of reinforcement learning have yielded a number of new algorithms for the prediction and control of Markovian environments. These algorithms, including the TD() algorithm of Sutton (1988) and the Q-learning algorithm of Watkins (1989), can be motivated heuristically as approximations to dynamic programming (DP). In this paper we provide a rigorous proof of convergence of these DP-based learning algorithms by relating them to the powerful techniques of stochastic approximation theory via a new convergence theorem. The theorem establishes a general class of convergent algorithms to which both TD() and Q-learning belong. This report describes research done at the Dept. of Brain and Cognitive Sciences, the Center for Biological and Computational Learning, and the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for CBCL is provided in part by a grant from the NSF (ASC-9217041). Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Dept. of Defense. The authors were supported by a grant from the McDonnell-Pew Foundation, by a grant from ATR Human Information Processing Research Laboratories, by a grant from Siemens Corporation, by by grant IRI-9013991 from the National Science Foundation, by grant N00014-90-J-1942 from the Office of Naval Research, and by NSF grant ECS-9216531 to support an Initiative in Intelligent Control at MIT. Michael I. Jordan is a NSF Presidential Young Investigator.

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 "A Unified Analysis of Value-Function-Based Reinforcement-Learning Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Learning to Act using Real-Time Dynamic Programming")
  Member "Issues in Using Function Approximation for Reinforcement Learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Learning to Act using Real-Time Dynamic Programming")
  Member "Reinforcement Learning with Soft State Aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "A Cognitive Model of Learning to Navigate")
  Member "Near-Optimal Performance for Reinforcement Learning in Polynomial Time":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Learning Curve Bounds for Markov Decision Processes with Undiscounted Rewards")
  Member "Reinforcement Learning Methods for Continuous-Time Markov Decision Problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "Learning to Act using Real-Time Dynamic Programming")
  Member "Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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