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 Bonuses and Dual Control"
  Abstract: Finding the Bayesian balance between exploration and exploitation in adaptive optimal control is in general intractable. This paper shows how to compute suboptimal estimates based on a certainty equivalence approximation arising from a form of dual control. This systematizes and extends existing uses of exploration bonuses in reinforcement learning (Sutton, 1990). The approach has two components: a statistical model of uncertainty in the world and a way of turning this into exploratory behaviour.

Connected through hyperedge HE-1 (induced by source paper "Neural Network Exploration Using Optimal Experiment Design"):
  with members:
    - "In:  A Mixture Model System for Medical and Machine Diagnosis"
    - "In  Active Learning with Statistical Models"
    - "Finding Promising Exploration Regions by Weighting Expected Navigation Costs continuous environments, some first-order approximations to"
    - "REINFORCEMENT DRIVEN INFORMATION ACQUISITION IN NON-DETERMINISTIC ENVIRONMENTS"
    - "Advances in Neural Information Processing Systems 8 Active Learning in Multilayer Perceptrons"
    - "An Algorithm for Active Data Collection for Learning Feasibility Study with Neural Networks."

Connected through hyperedge HE-2 (induced by source paper "Q-Learning for Bandit Problems"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms"

Connected through hyperedge HE-3 (induced by source paper "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"):
  with members:
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "Acting under Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation"
    - "Markov games as a framework for multi-agent reinforcement learning"
    - "Markov Decision Processes in Large State Spaces"
    - "A Unified Analysis of Value-Function-Based Reinforcement-Learning Algorithms"
    - "Asynchronous Modified Policy Iteration with Single-sided Updates"
    - "Auto-exploratory Average Reward Reinforcement Learning"
    - "On the Complexity of Solving Markov Decision Problems"

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