human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Submitted to NIPS96, Section: Applications. Preference: Oral presentation Reinforcement Learning for Dynamic Channel Allocation in":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Learning to Act using Real-Time Dynamic Programming")
  Member "Learning Team Strategies With Multiple Policy-Sharing Agents: A Soccer Case Study":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "DISCOVERING NEURAL NETS WITH LOW KOLMOGOROV COMPLEXITY AND HIGH GENERALIZATION CAPABILITY Neural Networks 10(5):857-873, 1997")
  Member "NEUROCONTROL BY REINFORCEMENT LEARNING":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Strategy Learning with Multilayer Connectionist Representations 1")
  Member "Spurious Solutions to the Bellman Equation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "MultiPlayer Residual Advantage Learning With General Function Approximation")
  Member "Self-Improving Factory Simulation using Continuous-time Average-Reward Reinforcement Learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Value Function Based Production Scheduling")
  Member "Reinforcement Learning Methods for Continuous-Time Markov Decision Problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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