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: "Auto-exploratory Average Reward Reinforcement Learning"
  Abstract: We introduce a model-based average reward Reinforcement Learning method called H-learning and compare it with its discounted counterpart, Adaptive Real-Time Dynamic Programming, in a simulated robot scheduling task. We also introduce an extension to H-learning, which automatically explores the unexplored parts of the state space, while always choosing greedy actions with respect to the current value function. We show that this "Auto-exploratory H-learning" performs better than the original H-learning under previously studied exploration methods such as random, recency-based, or counter-based exploration.

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"
    - "Exploration and Model Building in Mobile Robot Domains"

Connected through hyperedge HE-2 (induced by source paper "Reinforcement Learning Algorithms for Average-Payoff Markovian Decision Processes"):
  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,"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Learning Curve Bounds for Markov Decision Processes with Undiscounted Rewards"
    - "H-learning: A Reinforcement Learning Method to Optimize Undiscounted Average Reward"
    - "Self-Improving Factory Simulation using Continuous-time Average-Reward Reinforcement Learning"

Connected through hyperedge HE-3 (induced by source paper "Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "Generalization in Reinforcement Learning: Safely Approximating the Value Function"
    - "Using a Case Base of Surfaces to Speed-Up Reinforcement Learning"
    - "Generalized Prioritized Sweeping"
    - "Dynamic Belief Networks for Discrete Monitoring"

Connected through hyperedge HE-4 (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"
    - "On the Complexity of Solving Markov Decision Problems"
    - "Improving Policies without Measuring Merits"

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