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 "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"):
  Abstract: Miller, G. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review, 63(2):81-97. Schmidhuber, J. (1990b). Towards compositional learning with dynamic neural networks. Technical Report FKI-129-90, Technische Universitat Munchen, Institut fu Informatik. Servan-Schreiber, D., Cleermans, A., and McClelland, J. (1988). Encoding sequential structure in simple recurrent networks. Technical Report CMU-CS-88-183, Carnegie Mellon University, Computer Science Department.

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 "Decision Tree Function Approximation in Reinforcement Learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding")
  Member "USING MARKER-BASED GENETIC ENCODING OF NEURAL NETWORKS TO EVOLVE FINITE-STATE BEHAVIOUR":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Evolving Graphs and Networks with Edge Encoding: Preliminary Report")
  Member "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "LEARNING TO AVOID COLLISIONS: A REINFORCEMENT LEARNING PARADIGM FOR MOBILE ROBOT NAVIGATION")
  Member "A Modular Q-Learning Architecture for Manipulator Task Decomposition `Data storage in the cerebellar model ar":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks")
  Member "Living in a partially structured environment: How to bypass the limitations of classical reinforcement techniques":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Machine Learning,  Reinforcement Learning with Replacing Eligibility Traces")
  Member "A Neural Network Pole Balancer that Learns and Operates on a Real Robot in Real Time":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex.")
  Member "Q-Learning with Hidden-Unit Restarting":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "Reinforcement Learning with Modular Neural Networks for Control")

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