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: "Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis complexity grows, the learning task"
  Abstract: difficult. We face this problem using an architecture based on learning classifier systems and on the description of the learning technique used and of the organizational structure proposed, we present experiments that show how behaviour acquisition can be achieved. Our simulated robot learns to structural properties of animal behavioural organization, as proposed by ethologists. After a

Connected through hyperedge HE-1 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."):
  with members:
    - "Measuring the Difficulty of Specific Learning Problems"
    - "Trading Spaces: Computation, Representation and the Limits of Uninformed Learning"
    - "Discovering Complex Othello Strategies Through Evolutionary Neural Networks"
    - "USING MARKER-BASED GENETIC ENCODING OF NEURAL NETWORKS TO EVOLVE FINITE-STATE BEHAVIOUR"
    - "Evolving Obstacle Avoidance Behavior in a Robot Arm"
    - "A Comparison of Selection Schemes used in Genetic Algorithms"
    - "Determining Successful Negotiation Strategies: An Evolutionary Approach"
    - "Genetic Programming and Redundancy"

Connected through hyperedge HE-2 (induced by source paper "Learning Sequential Decision Rules Using Simulation Models and Competition"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "The Exploitation of Cooperation in Iterated Prisoner's Dilemma"
    - "USING A GENETIC ALGORITHM TO LEARN BEHAVIORS FOR AUTONOMOUS VEHICLES"
    - "Evolutionary Neural Networks for Value Ordering in Constraint Satisfaction Problems"
    - "Using a Genetic Algorithm to Learn Strategies for Collision Avoidance and Local Navigation"
    - "A Coevolutionary Approach to Learning Sequential Decision Rules"
    - "GENETIC AND NON GENETIC OPERATORS IN ALECSYS"

Connected through hyperedge HE-3 (induced by source paper "EVOLVING ROBOT BEHAVIORS"):
  with members:
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"
    - "Using a Genetic Algorithm to Learn Strategies for Collision Avoidance and Local Navigation"
    - "Improving Tactical Plans with Genetic Algorithms"
    - "Simulation-Assisted Learning by Competition: Effects of Noise Differences Between Training Model and Target Environment"

Connected through hyperedge HE-4 (induced by source paper "EVOLUTIONARY ALGORITHMS IN ROBOTICS"):
  with members:
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"
    - "Improving Tactical Plans with Genetic Algorithms"
    - "Simulation-Assisted Learning by Competition: Effects of Noise Differences Between Training Model and Target Environment"

Connected through hyperedge HE-5 (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"
    - "The Role of the Trainer in Reinforcement Learning"
    - "A Study of the Generalization Capabilities of XCS"
    - "ROBO-SHEPHERD: LEARNING COMPLEX ROBOTIC BEHAVIORS"

Connected through hyperedge HE-6 (induced by source paper "The Role of the Trainer in Reinforcement Learning"):
  with members:
    - "Robot Shaping: Developing Situated Agents through Learning"
    - "GENETIC AND NON GENETIC OPERATORS IN ALECSYS"

Connected through hyperedge HE-7 (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"
    - "Coordinating Reactive Behaviors  keywords: reactive systems, planning and learning"
    - "REINFORCEMENT LEARNING FOR COORDINATED REACTIVE CONTROL"
    - "The Role of the Trainer in Reinforcement Learning"
    - "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