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 "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.

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 "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "TDLeaf(): Combining Temporal Difference learning with game-tree search.")
  Member "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "LEARNING TO AVOID COLLISIONS: A REINFORCEMENT LEARNING PARADIGM FOR MOBILE ROBOT NAVIGATION")
  Member "Learning Curve Bounds for Markov Decision Processes with Undiscounted Rewards":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Learning to Act using Real-Time Dynamic Programming")
  Member "Auto-exploratory Average Reward Reinforcement Learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Learning to Act using Real-Time Dynamic Programming")
  Member "H-learning: A Reinforcement Learning Method to Optimize Undiscounted Average Reward":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Learning to Act using Real-Time Dynamic Programming")
  Member "Self-Improving Factory Simulation using Continuous-time Average-Reward Reinforcement Learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "Value Function Based Production Scheduling")

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