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: "Asynchronous Modified Policy Iteration with Single-sided Updates"
  Abstract: We present a new algorithm for solving Markov decision problems that extends the modified policy iteration algorithm of Puterman and Shin [6] in two important ways: 1) The new algorithm is asynchronous in that it allows the values of states to be updated in arbitrary order, and it does not need to consider all actions in each state while updating the policy. 2) The new algorithm converges under more general initial conditions than those required by modified policy iteration. Specifically, the set of initial policy-value function pairs for which our algorithm guarantees convergence is a strict superset of the set for which modified policy iteration converges. This generalization was obtained by making a simple and easily implementable change to the policy evaluation operator used in updating the value function. Both the asynchronous nature of our algorithm and its convergence under more general conditions expand the range of problems to which our algorithm can be applied.

Connected through hyperedge HE-1 (induced by source paper "Analysis of Some Incremental Variants of Policy Iteration: First Steps Toward Understanding Actor-Critic Learning Systems"):
  with members:
    - "An Upper Bound on the Loss from Approximate Optimal-Value Functions"

Connected through hyperedge HE-2 (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"
    - "Auto-exploratory Average Reward Reinforcement Learning"
    - "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