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: "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
  Abstract: The problem of maximizing the expected total discounted reward in a completely observable Markovian environment, i.e., a Markov decision process (mdp), models a particular class of sequential decision problems. Algorithms have been developed for making optimal decisions in mdps given either an mdp specification or the opportunity to interact with the mdp over time. Recently, other sequential decision-making problems have been studied prompting the development of new algorithms and analyses. We describe a new generalized model that subsumes mdps as well as many of the recent variations. We prove some basic results concerning this model and develop generalizations of value iteration, policy iteration, model-based reinforcement-learning, and Q-learning that can be used to make optimal decisions in the generalized model under various assumptions. Applications of the theory to particular models are described, including risk-averse mdps, exploration-sensitive mdps, sarsa, Q-learning with spreading, two-player games, and approximate max picking via sampling. Central to the results are the contraction property of the value operator and a stochastic-approximation theorem that reduces asynchronous convergence to synchronous convergence.

Connected through hyperedge HE-1 (induced by source paper "Acting under Uncertainty: Discrete Bayesian Models for Mobile-Robot Navigation"):
  with members:
    - "Learning policies for partially observable environments: Scaling up"
    - "Approximating Optimal Policies for Partially Observable Stochastic Domains"
    - "Learning sorting and decision trees with POMDPs"

Connected through hyperedge HE-2 (induced by source paper "On the Complexity of Solving Markov Decision Problems"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "Optimal Navigation in a Probibalistic World"
    - "Tight Performance Bounds on Greedy Policies Based on Imperfect Value Functions"
    - "The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces"

Connected through hyperedge HE-3 (induced by source paper "Improving Policies without Measuring Merits"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"

Connected through hyperedge HE-4 (induced by source paper "Learning to Achieve Goals"):
  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,"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks"
    - "Finding Promising Exploration Regions by Weighting Expected Navigation Costs continuous environments, some first-order approximations to"

Connected through hyperedge HE-5 (induced by source paper "A Unified Analysis of Value-Function-Based Reinforcement-Learning Algorithms"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "Machine Learning,  Reinforcement Learning with Replacing Eligibility Traces"
    - "Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm"
    - "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms"
    - "Multi-criteria reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"):
  with members:
    - "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"
    - "Using a Case Base of Surfaces to Speed-Up Reinforcement Learning"
    - "Exploration and Model Building in Mobile Robot Domains"
    - "Strategy Learning with Multilayer Connectionist Representations 1"
    - "Least-Squares Temporal Difference Learning"
    - "An Upper Bound on the Loss from Approximate Optimal-Value Functions"
    - "Monitoring in Embedded Agents"

Connected through hyperedge HE-7 (induced by source paper "Asynchronous Modified Policy Iteration with Single-sided Updates"):
  with members:
    - "Analysis of Some Incremental Variants of Policy Iteration: First Steps Toward Understanding Actor-Critic Learning Systems"

Connected through hyperedge HE-8 (induced by source paper "MultiPlayer Residual Advantage Learning With General Function Approximation"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Generalization in Reinforcement Learning: Safely Approximating the Value Function"
    - "Adapting Bias by Gradient Descent: An Incremental Version of Delta-Bar-Delta"
    - "Spurious Solutions to the Bellman Equation"
    - "Metrics for Temporal Difference Learning"
    - "Residual Q-Learning Applied to Visual Attention"

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