human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent groups of papers sharing a common citing paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "Learning to Act using Real-Time Dynamic Programming"):
  with members:
    - "Submitted to NIPS96, Section: Applications. Preference: Oral presentation Reinforcement Learning for Dynamic Channel Allocation in"
    - "Active Gesture Recognition using Partially Observable Markov Decision Processes"
    - "Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function"
    - "Learning sorting and decision trees with POMDPs"
    - "Markov Decision Processes in Large State Spaces"
    - "Learning Curve Bounds for Markov Decision Processes with Undiscounted Rewards"
    - "The Efficient Learning of Multiple Task Sequences"
    - "A Unified Analysis of Value-Function-Based Reinforcement-Learning Algorithms"

Connected through hyperedge HE-2 (induced by source paper "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Using a Case Base of Surfaces to Speed-Up Reinforcement Learning"
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "Strategy Learning with Multilayer Connectionist Representations 1"
    - "Least-Squares Temporal Difference Learning"
    - "An Upper Bound on the Loss from Approximate Optimal-Value Functions"
    - "Monitoring in Embedded Agents"

Connected through hyperedge HE-3 (induced by source paper "A Modular Q-Learning Architecture for Manipulator Task Decomposition `Data storage in the cerebellar model ar"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "The Efficient Learning of Multiple Task Sequences"
    - "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks"

Connected through hyperedge HE-4 (induced by source paper "The Influence of Domain Properties on the Performance of Real-Time Search Algorithms"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"

Connected through hyperedge HE-5 (induced by source paper "Lazy Acquisition of Place Knowledge"):
  with members:
    - "(1994); Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Case-Based Reasoning: Foundational Issues, Methodological"
    - "Case-Based Acquisition of Place Knowledge"

Connected through hyperedge HE-6 (induced by source paper "The Efficient Learning of Multiple Task Sequences"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks"
    - "A Modular Q-Learning Architecture for Manipulator Task Decomposition `Data storage in the cerebellar model ar"

Connected through hyperedge HE-7 (induced by source paper "Case-Based Acquisition of Place Knowledge"):
  with members:
    - "Lazy Acquisition of Place Knowledge"

Connected through hyperedge HE-8 (induced by source paper "LEARNING BY ERROR-DRIVEN DECOMPOSITION"):
  with members:
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."
    - "Representation and Evolution of Neural Networks"

Question: Please tell me which class the center node 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