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: "Evolving Obstacle Avoidance Behavior in a Robot Arm"
  Abstract: Existing approaches for learning to control a robot arm rely on supervised methods where correct behavior is explicitly given. It is difficult to learn to avoid obstacles using such methods, however, because examples of obstacle avoidance behavior are hard to generate. This paper presents an alternative approach that evolves neural network controllers through genetic algorithms. No input/output examples are necessary, since neuro-evolution learns from a single performance measurement over the entire task of grasping an object. The approach is tested in a simulation of the OSCAR-6 robot arm which receives both visual and sensory input. Neural networks evolved to effectively avoid obstacles at various locations to reach random target locations.

Connected through hyperedge HE-1 (induced by source paper "2-D Pole Balancing with Recurrent Evolutionary Networks"):
  with members:
    - "Machine Learning,  Efficient Reinforcement Learning through Symbiotic Evolution"
    - "Incremental Evolution of Complex General Behavior"

Connected through hyperedge HE-2 (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"
    - "A Comparison of Selection Schemes used in Genetic Algorithms"
    - "Determining Successful Negotiation Strategies: An Evolutionary Approach"
    - "Genetic Programming and Redundancy"
    - "Island Model Genetic Algorithms and Linearly Separable Problems"

Connected through hyperedge HE-3 (induced by source paper "Issues in Evolutionary Robotics"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "The Evolutionary Cost of Learning"
    - "Analysis of Neurocontrollers Designed by Simulated Evolution"
    - "Selection for Wandering Behavior in a Small Robot"
    - "A Hybrid GP/GA Approach for Co-evolving Controllers and Robot Bodies to Achieve Fitness-Specified Tasks"
    - "Environmental Effects on Minimal Behaviors in the Minimat World"
    - "Evolving nonTrivial Behaviors on Real Robots: an Autonomous Robot that Picks up Objects"
    - "Evolving Visually Guided Robots"

Connected through hyperedge HE-4 (induced by source paper "Machine Learning,  Efficient Reinforcement Learning through Symbiotic Evolution"):
  with members:
    - "Real-time Interactive Neuro-evolution"
    - "Evolutionary Neural Networks for Value Ordering in Constraint Satisfaction Problems"
    - "2-D Pole Balancing with Recurrent Evolutionary Networks"
    - "EVOLVING NEURAL NETWORKS WITH COLLABORATIVE SPECIES"
    - "A Coevolutionary Approach to Learning Sequential Decision Rules"

Connected through hyperedge HE-5 (induced by source paper "Hierarchical Evolution of Neural Networks"):
  with members:
    (none)

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