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: "Environmental Effects on Minimal Behaviors in the Minimat World"
  Abstract: The structure of an environment affects the behaviors of the organisms that have evolved in it. How is that structure to be described, and how can its behavioral consequences be explained and predicted? We aim to establish initial answers to these questions by simulating the evolution of very simple organisms in simple environments with different structures. Our artificial creatures, called "minimats," have neither sensors nor memory and behave solely by picking amongst the actions of moving, eating, reproducing, and sitting, according to an inherited probability distribution. Our simulated environments contain only food (and multiple minimats) and are structured in terms of their spatial and temporal food density and the patchiness with which the food appears. Changes in these environmental parameters affect the evolved behaviors of minimats in different ways, and all three parameters are of importance in describing the minimat world. One of the most useful behavioral strategies that evolves is "looping" movement, which allows minimats-despite their lack of internal state-to match their behavior to the temporal (and spatial) structure of their environment. Ultimately we find that minimats construct their own environments through their individual behaviors, making the study of the impact of global environment structure on individual behavior much more complex.

Connected through hyperedge HE-1 (induced by source paper "Complex Environments to Complex Behaviors  FROM COMPLEX ENVIRONMENTS TO COMPLEX BEHAVIORS"):
  with members:
    - "Local Selection"

Connected through hyperedge HE-2 (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."
    - "Evolving Obstacle Avoidance Behavior in a Robot Arm"
    - "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"
    - "Evolving nonTrivial Behaviors on Real Robots: an Autonomous Robot that Picks up Objects"
    - "Evolving Visually Guided Robots"

Connected through hyperedge HE-3 (induced by source paper "Generalist and Specialist Behavior Due to Individual Energy Extracting Abilities."):
  with members:
    - "Specialization under Social Conditions in Shared Environments"

Connected through hyperedge HE-4 (induced by source paper "EVOLVING SENSORS IN ENVIRONMENTS OF CONTROLLED COMPLEXITY"):
  with members:
    - "Landscapes, Learning Costs and Genetic Assimilation."
    - "Guiding or Hiding: Explorations into the Effects of Learning on the Rate of Evolution."
    - "Generalization and scaling in reinforcement learning"

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