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: "Self-Improving Factory Simulation using Continuous-time Average-Reward Reinforcement Learning"
  Abstract: Many factory optimization problems, from inventory control to scheduling and reliability, can be formulated as continuous-time Markov decision processes. A primary goal in such problems is to find a gain-optimal policy that minimizes the long-run average cost. This paper describes a new average-reward algorithm called SMART for finding gain-optimal policies in continuous time semi-Markov decision processes. The paper presents a detailed experimental study of SMART on a large unreliable production inventory problem. SMART outperforms two well-known reliability heuristics from industrial engineering. A key feature of this study is the integration of the reinforcement learning algorithm directly into two commercial discrete-event simulation packages, ARENA and CSIM, paving the way for this approach to be applied to many other factory optimization problems for which there already exist simulation models.

Connected through hyperedge HE-1 (induced by source paper "Value Function Based Production Scheduling"):
  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,"
    - "A Reinforcement Learning Approach to Job-shop Scheduling"
    - "Efficient Locally Weighted Polynomial Regression Predictions"

Connected through hyperedge HE-2 (induced by source paper "Reinforcement Learning Algorithms for Average-Payoff Markovian Decision Processes"):
  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"
    - "Learning Curve Bounds for Markov Decision Processes with Undiscounted Rewards"
    - "Auto-exploratory Average Reward Reinforcement Learning"
    - "H-learning: A Reinforcement Learning Method to Optimize Undiscounted Average Reward"

Connected through hyperedge HE-3 (induced by source paper "In  Improving Elevator Performance Using Reinforcement Learning"):
  with members:
    - "Submitted to NIPS96, Section: Applications. Preference: Oral presentation Reinforcement Learning for Dynamic Channel Allocation in"
    - "Learning Team Strategies With Multiple Policy-Sharing Agents: A Soccer Case Study"
    - "NEUROCONTROL BY REINFORCEMENT LEARNING"
    - "Spurious Solutions to the Bellman Equation"
    - "Reinforcement Learning Methods for Continuous-Time Markov Decision Problems"

Connected through hyperedge HE-4 (induced by source paper "Reinforcement Learning Methods for Continuous-Time Markov Decision Problems"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "In  Improving Elevator Performance Using Reinforcement Learning"
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms"

Connected through hyperedge HE-5 (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-6 (induced by source paper "Q2: Memory-based active learning for optimizing noisy continuous functions field expands beyond prediction and function"):
  with members:
    - "In  Active Learning with Statistical Models"
    - "Memory Based Stochastic Optimization for Validation and Tuning of Function Approximators"

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