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: "Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function"
  Abstract: Almost all the work in Average-reward Re- inforcement Learning (ARL) so far has focused on table-based methods which do not scale to domains with large state spaces. In this paper, we propose two extensions to a model-based ARL method called H-learning to address the scale-up problem. We extend H-learning to learn action models and reward functions in the form of Bayesian networks, and approximate its value function using local linear regression. We test our algorithms on several scheduling tasks for a simulated Automatic Guided Vehicle (AGV) and show that they are effective in significantly reducing the space requirement of H-learning and making it converge faster. To the best of our knowledge, our results are the first in apply

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"
    - "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"
    - "A Unified Analysis of Value-Function-Based Reinforcement-Learning Algorithms"

Connected through hyperedge HE-2 (induced by source paper "Using a Case Base of Surfaces to Speed-Up Reinforcement Learning"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "(1994); Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Case-Based Reasoning: Foundational Issues, Methodological"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "A Teaching Strategy for Memory-Based Control"

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"
    - "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"
    - "Tight Performance Bounds on Greedy Policies Based on Imperfect Value Functions"

Connected through hyperedge HE-4 (induced by source paper "Dynamic Belief Networks for Discrete Monitoring"):
  with members:
    - "Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks"
    - "State-Space Abstraction for Anytime Evaluation of Probabilistic Networks"
    - "Introduction to the Special Section on Knowledge-Based Construction of Probabilistic and Decision Models (IEEE Transactions"
    - "Stochastic simulation algorithms for dynamic probabilistic networks"
    - "Fall Diagnosis using Dynamic Belief Networks"

Connected through hyperedge HE-5 (induced by source paper "Generalized Prioritized Sweeping"):
  with members:
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "Sequential Update of Bayesian Network Structure"
    - "Tight Performance Bounds on Greedy Policies Based on Imperfect Value Functions"
    - "A Tutorial on Learning With Bayesian Networks"

Connected through hyperedge HE-6 (induced by source paper "Auto-exploratory Average Reward Reinforcement Learning"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "Reinforcement Learning Algorithms for Average-Payoff Markovian Decision Processes"

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