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: "Guiding or Hiding: Explorations into the Effects of Learning on the Rate of Evolution."
  Abstract: Individual lifetime learning can `guide' an evolving population to areas of high fitness in genotype space through an evolutionary phenomenon known as the Baldwin effect (Baldwin, 1896; Hin-ton & Nowlan, 1987). It is the accepted wisdom that this guiding speeds up the rate of evolution. By highlighting another interaction between learning and evolution, that will be termed the Hiding effect, it will be argued here that this depends on the measure of evolutionary speed one adopts. The Hiding effect shows that learning can reduce the selection pressure between individuals by `hiding' their genetic differences. There is thus a trade-off between the Baldwin effect and the Hiding effect to determine learning's influence on evolution and two factors that contribute to this trade-off, the cost of learning and landscape epis tasis, are investigated experimentally.

Connected through hyperedge HE-1 (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."
    - "Issues in Evolutionary Robotics"
    - "Landscapes, Learning Costs and Genetic Assimilation."

Connected through hyperedge HE-2 (induced by source paper "EVOLVING SENSORS IN ENVIRONMENTS OF CONTROLLED COMPLEXITY"):
  with members:
    - "Environmental Effects on Minimal Behaviors in the Minimat World"
    - "Landscapes, Learning Costs and Genetic Assimilation."
    - "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