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: "Issues in Evolutionary Robotics"
  Abstract: A version of this paper appears in: Proceedings of SAB92, the Second International Conference on Simulation of Adaptive Behaviour J.-A. Meyer, H. Roitblat, and S. Wilson, editors, MIT Press Bradford Books, Cambridge, MA, 1993.

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 "Environmental Effects on Minimal Behaviors in the Minimat World"):
  with members:
    - "EVOLVING SENSORS IN ENVIRONMENTS OF CONTROLLED COMPLEXITY"
    - "Complex Environments to Complex Behaviors  FROM COMPLEX ENVIRONMENTS TO COMPLEX BEHAVIORS"
    - "Generalist and Specialist Behavior Due to Individual Energy Extracting Abilities."

Connected through hyperedge HE-3 (induced by source paper "The Evolutionary Cost of Learning"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Landscapes, Learning Costs and Genetic Assimilation."
    - "Guiding or Hiding: Explorations into the Effects of Learning on the Rate of Evolution."

Connected through hyperedge HE-4 (induced by source paper "Evolving nonTrivial Behaviors on Real Robots: an Autonomous Robot that Picks up Objects"):
  with members:
    - "Selection for Wandering Behavior in a Small Robot"
    - "An Artificial Life Model for Investigating the Evolution of Modularity"

Connected through hyperedge HE-5 (induced by source paper "Evolving Visually Guided Robots"):
  with members:
    - "Adapting Control Strategies for Situated Autonomous Agents"
    - "Evolving Visual Routines"

Connected through hyperedge HE-6 (induced by source paper "Evolving Obstacle Avoidance Behavior in a Robot Arm"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Hierarchical Evolution of Neural Networks"
    - "Machine Learning,  Efficient Reinforcement Learning through Symbiotic Evolution"
    - "2-D Pole Balancing with Recurrent Evolutionary Networks"

Connected through hyperedge HE-7 (induced by source paper "Analysis of Neurocontrollers Designed by Simulated Evolution"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Properties of Genetic Representations of Neural Architectures"
    - "The Automatic Programming of Agents that Learn Mental Models and Create Simple Plans of Action"

Connected through hyperedge HE-8 (induced by source paper "Selection for Wandering Behavior in a Small Robot"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Evolving nonTrivial Behaviors on Real Robots: an Autonomous Robot that Picks up Objects"

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: Genetic_Algorithms