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: "Reinforcement Learning Algorithms for Average-Payoff Markovian Decision Processes"
  Abstract: Reinforcement learning (RL) has become a central paradigm for solving learning-control problems in robotics and artificial intelligence. RL researchers have focussed almost exclusively on problems where the controller has to maximize the discounted sum of payoffs. However, as emphasized by Schwartz (1993), in many problems, e.g., those for which the optimal behavior is a limit cycle, it is more natural and com-putationally advantageous to formulate tasks so that the controller's objective is to maximize the average payoff received per time step. In this paper I derive new average-payoff RL algorithms as stochastic approximation methods for solving the system of equations associated with the policy evaluation and optimal control questions in average-payoff RL tasks. These algorithms are analogous to the popular TD and Q-learning algorithms already developed for the discounted-payoff case. One of the algorithms derived here is a significant variation of Schwartz's R-learning algorithm. Preliminary empirical results are presented to validate these new algorithms.

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 "Learning Curve Bounds for Markov Decision Processes with Undiscounted Rewards"):
  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,"
    - "Rigorous Learning Curve Bounds from Statistical Mechanics"
    - "Markov Decision Processes in Large State Spaces"
    - "Near-Optimal Performance for Reinforcement Learning in Polynomial Time"

Connected through hyperedge HE-3 (induced by source paper "Self-Improving Factory Simulation using Continuous-time Average-Reward Reinforcement Learning"):
  with members:
    - "In  Improving Elevator Performance Using Reinforcement Learning"
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Value Function Based Production Scheduling"
    - "Reinforcement Learning Methods for Continuous-Time Markov Decision Problems"
    - "Q2: Memory-based active learning for optimizing noisy continuous functions field expands beyond prediction and function"

Connected through hyperedge HE-4 (induced by source paper "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"):
  with members:
    - "Submitted to NIPS96, Section: Applications. Preference: Oral presentation Reinforcement Learning for Dynamic Channel Allocation in"
    - "High-Performance Job-Shop Scheduling With A Time-Delay TD() Network"
    - "Active Gesture Recognition using Partially Observable Markov Decision Processes"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Generalization in Reinforcement Learning: Safely Approximating the Value Function"
    - "Using a Case Base of Surfaces to Speed-Up Reinforcement Learning"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "A Reinforcement Learning Approach to Job-shop Scheduling"

Connected through hyperedge HE-5 (induced by source paper "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Decision Tree Function Approximation in Reinforcement Learning"
    - "USING MARKER-BASED GENETIC ENCODING OF NEURAL NETWORKS TO EVOLVE FINITE-STATE BEHAVIOUR"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "A Modular Q-Learning Architecture for Manipulator Task Decomposition `Data storage in the cerebellar model ar"
    - "Living in a partially structured environment: How to bypass the limitations of classical reinforcement techniques"
    - "A Neural Network Pole Balancer that Learns and Operates on a Real Robot in Real Time"
    - "Q-Learning with Hidden-Unit Restarting"

Connected through hyperedge HE-6 (induced by source paper "H-learning: A Reinforcement Learning Method to Optimize Undiscounted Average Reward"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"

Connected through hyperedge HE-7 (induced by source paper "Auto-exploratory Average Reward Reinforcement Learning"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function"
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"

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