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: "Complex Environments to Complex Behaviors  FROM COMPLEX ENVIRONMENTS TO COMPLEX BEHAVIORS"
  Abstract: Adaptation of ecological systems to their environments is commonly viewed through some explicit fitness function defined a priori by the experimenter, or measured a posteriori by estimations based on population size and/or reproductive rates. These methods do not capture the role of environmental complexity in shaping the selective pressures that control the adaptive process. Ecological simulations enabled by computational tools such as the Latent Energy Environments (LEE) model allow us to characterize more closely the effects of environmental complexity on the evolution of adaptive behaviors. LEE is described in this paper. Its motivation arises from the need to vary complexity in controlled and predictable ways, without assuming the relationship of these changes to the adaptive behaviors they engender. This goal is achieved through a careful characterization of environments in which different forms of "energy" are well-defined. A genetic algorithm using endogenous fitness and local selection is used to model the evolutionary process. Individuals in the population are modeled by neural networks with simple sensory-motor systems, and variations in their behaviors are related to interactions with varying environments. We outline the results of three experiments that analyze different sources of environmental complexity and their effects on the collective behaviors of evolving populations.

Connected through hyperedge HE-1 (induced by source paper "Environmental Effects on Minimal Behaviors in the Minimat World"):
  with members:
    - "Issues in Evolutionary Robotics"
    - "EVOLVING SENSORS IN ENVIRONMENTS OF CONTROLLED COMPLEXITY"
    - "Generalist and Specialist Behavior Due to Individual Energy Extracting Abilities."

Connected through hyperedge HE-2 (induced by source paper "Local Selection"):
  with members:
    - "An Analysis of the Effects of Neighborhood Size and Shape on Local Selection Algorithms"

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