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 "Exploration and Model Building in Mobile Robot Domains"):
  Abstract: I present first results on COLUMBUS, an autonomous mobile robot. COLUMBUS operates in initially unknown, structured environments. Its task is to explore and model the environment efficiently while avoiding collisions with obstacles. COLUMBUS uses an instance-based learning technique for modeling its environment. Real-world experiences are generalized via two artificial neural networks that encode the characteristics of the robot's sensors, as well as the characteristics of typical environments the robot is assumed to face. Once trained, these networks allow for knowledge transfer across different environments the robot will face over its lifetime. COLUMBUS' models represent both the expected reward and the confidence in these expectations. Exploration is achieved by navigating to low confidence regions. An efficient dynamic programming method is employed in background to find minimal-cost paths that, executed by the robot, maximize exploration. COLUMBUS operates in real-time. It has been operating successfully in an office building environment for periods up to hours.

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 "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "LEARNING TO AVOID COLLISIONS: A REINFORCEMENT LEARNING PARADIGM FOR MOBILE ROBOT NAVIGATION")
  Member "The Efficient Learning of Multiple Task Sequences":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks")
  Member "A Modular Q-Learning Architecture for Manipulator Task Decomposition `Data storage in the cerebellar model ar":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks")
  Member "Lazy Acquisition of Place Knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "(1994); Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Case-Based Reasoning: Foundational Issues, Methodological")
  Member "LEARNING BY ERROR-DRIVEN DECOMPOSITION":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Representation and Evolution of Neural Networks")
  Member "The Influence of Domain Properties on the Performance of Real-Time Search Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "Learning to Act using Real-Time Dynamic Programming")
  Member "Case-Based Acquisition of Place Knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "Lazy Acquisition of Place Knowledge")

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