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: "The Role of the Trainer in Reinforcement Learning"
  Abstract: In this paper we propose a threestage incremental approach to the development of autonomous agents. We discuss some issues about the characteristics which differentiate reinforcement programs (RPs), and define the trainer as a particular kind of RP. We present a set of results obtained running experiments with a trainer which provides guidance to the AutonoMouse, our mousesized autonomous robot.

Connected through hyperedge HE-1 (induced by source paper "GENETIC AND NON GENETIC OPERATORS IN ALECSYS"):
  with members:
    - "Robot Shaping: Developing Situated Agents through Learning"
    - "On the Relations Between Search and Evolutionary Algorithms"
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"
    - "Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis complexity grows, the learning task"
    - "A Study of the Generalization Capabilities of XCS"
    - "ROBO-SHEPHERD: LEARNING COMPLEX ROBOTIC BEHAVIORS"

Connected through hyperedge HE-2 (induced by source paper "Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis complexity grows, the learning task"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Robot Shaping: Developing Situated Agents through Learning"
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"
    - "GENETIC AND NON GENETIC OPERATORS IN ALECSYS"
    - "EVOLVING ROBOT BEHAVIORS"
    - "EVOLUTIONARY ALGORITHMS IN ROBOTICS"
    - "An Evolutionary Approach to Learning in Robots"

Connected through hyperedge HE-3 (induced by source paper "Robot Shaping: Developing Situated Agents through Learning"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Learning to Race: Experiments with a Simulated Race Car"
    - "Adapting Control Strategies for Situated Autonomous Agents"
    - "Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis complexity grows, the learning task"
    - "Coordinating Reactive Behaviors  keywords: reactive systems, planning and learning"
    - "REINFORCEMENT LEARNING FOR COORDINATED REACTIVE CONTROL"
    - "An unsupervised neural network for low-level control of a wheeled mobile robot: noise resistance, stability,"

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