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: "Tight Performance Bounds on Greedy Policies Based on Imperfect Value Functions"
  Abstract: Consider a given value function on states of a Markov decision problem, as might result from applying a reinforcement learning algorithm. Unless this value function equals the corresponding optimal value function, at some states there will be a discrepancy, which is natural to call the Bellman residual, between what the value function specifies at that state and what is obtained by a one-step lookahead along the seemingly best action at that state using the given value function to evaluate all succeeding states. This paper derives a tight bound on how far from optimal the discounted return for a greedy policy based on the given value function will be as a function of the maximum norm magnitude of this Bellman residual. A corresponding result is also obtained for value functions defined on state-action pairs, as are used in Q-learning. One significant application of these results is to problems where a function approximator is used to learn a value function, with training of the approxi-mator based on trying to minimize the Bellman residual across states or state-action pairs. When control is based on the use of the resulting value function, this result provides a link between how well the objectives of function approximator training are met and the quality of the resulting control.

Connected through hyperedge HE-1 (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"
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces"

Connected through hyperedge HE-2 (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"
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "Generalization in Reinforcement Learning: Safely Approximating the Value Function"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Decision Tree Function Approximation in Reinforcement Learning"
    - "Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function"
    - "A Reinforcement Learning Approach to Job-shop Scheduling"
    - "Value Function Approximations and Job-Shop Scheduling"
    - "Robust Value Function Approximation by Working Backwards Computing an accurate value function is the key"
    - "An Upper Bound on the Loss from Approximate Optimal-Value Functions"
    - "Issues in Using Function Approximation for Reinforcement Learning"

Connected through hyperedge HE-4 (induced by source paper "Generalized Prioritized Sweeping"):
  with members:
    - "Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "Sequential Update of Bayesian Network Structure"
    - "A Tutorial on Learning With Bayesian Networks"

Connected through hyperedge HE-5 (induced by source paper "Issues in Using Function Approximation for Reinforcement Learning"):
  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,"
    - "Generalization in Reinforcement Learning: Safely Approximating the Value Function"
    - "An Upper Bound on the Loss from Approximate Optimal-Value Functions"
    - "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms"
    - "Learning To Play the Game of Chess"
    - "Locally Weighted Learning for Control"

Connected through hyperedge HE-6 (induced by source paper "Planning by Incremental Dynamic Programming"):
  with members:
    - "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"
    - "Learning Optimal Dialogue Strategies: A Case Study of a Spoken Dialogue Agent for Email"

Connected through hyperedge HE-7 (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-8 (induced by source paper "An Upper Bound on the Loss from Approximate Optimal-Value Functions"):
  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"
    - "Generalization in Reinforcement Learning: Safely Approximating the Value Function"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "Analysis of Some Incremental Variants of Policy Iteration: First Steps Toward Understanding Actor-Critic Learning Systems"
    - "Issues in Using Function Approximation for Reinforcement Learning"

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