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: "In  Improving Elevator Performance Using Reinforcement Learning"
  Abstract: This paper describes the application of reinforcement learning (RL) to the difficult real world problem of elevator dispatching. The elevator domain poses a combination of challenges not seen in most RL research to date. Elevator systems operate in continuous state spaces and in continuous time as discrete event dynamic systems. Their states are not fully observable and they are nonstationary due to changing passenger arrival rates. In addition, we use a team of RL agents, each of which is responsible for controlling one elevator car. The team receives a global reinforcement signal which appears noisy to each agent due to the effects of the actions of the other agents, the random nature of the arrivals and the incomplete observation of the state. In spite of these complications, we show results that in simulation surpass the best of the heuristic elevator control algorithms of which we are aware. These results demonstrate the power of RL on a very large scale stochastic dynamic optimization problem of practical utility.

Connected through hyperedge HE-1 (induced by source paper "Spurious Solutions to the Bellman Equation"):
  with members:
    - "MultiPlayer Residual Advantage Learning With General Function Approximation"

Connected through hyperedge HE-2 (induced by source paper "Self-Improving Factory Simulation using Continuous-time Average-Reward Reinforcement Learning"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Value Function Based Production Scheduling"
    - "Reinforcement Learning Algorithms for Average-Payoff Markovian Decision Processes"
    - "Reinforcement Learning Methods for Continuous-Time Markov Decision Problems"
    - "Q2: Memory-based active learning for optimizing noisy continuous functions field expands beyond prediction and function"

Connected through hyperedge HE-3 (induced by source paper "Learning Team Strategies With Multiple Policy-Sharing Agents: A Soccer Case Study"):
  with members:
    - "Markov games as a framework for multi-agent reinforcement learning"
    - "DISCOVERING NEURAL NETS WITH LOW KOLMOGOROV COMPLEXITY AND HIGH GENERALIZATION CAPABILITY Neural Networks 10(5):857-873, 1997"

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"
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms"
    - "Self-Improving Factory Simulation using Continuous-time Average-Reward Reinforcement Learning"

Connected through hyperedge HE-5 (induced by source paper "NEUROCONTROL BY REINFORCEMENT LEARNING"):
  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"
    - "Strategy Learning with Multilayer Connectionist Representations 1"
    - "Optimal Attitude Control of Satellites by Artificial Neural Networks: a Pilot Study"

Connected through hyperedge HE-6 (induced by source paper "Submitted to NIPS96, Section: Applications. Preference: Oral presentation Reinforcement Learning for Dynamic Channel Allocation in"):
  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,"
    - "High-Performance Job-Shop Scheduling With A Time-Delay TD() Network"

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