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: "Landscapes, Learning Costs and Genetic Assimilation."
  Abstract: The evolution of a population can be guided by phenotypic traits acquired by members of that population during their lifetime. This phenomenon, known as the Baldwin Effect, can speed the evolutionary process as traits that are initially acquired become genetically specified in later generations. This paper presents conditions under which this genetic assimilation can take place. As well as the benefits that lifetime adaptation can give a population, there may be a cost to be paid for that adaptive ability. It is the evolutionary trade-off between these costs and benefits that provides the selection pressure for acquired traits to become genetically specified. It is also noted that genotypic space, in which evolution operates, and phenotypic space, on which adaptive processes (such as learning) operate, are, in general, of a different nature. To guarantee an acquired characteristic can become genetically specified, then these spaces must have the property of neighbourhood correlation which means that a small distance between two individuals in phenotypic space implies that there is a small distance between the same two individuals in genotypic space.

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"
    - "Guiding or Hiding: Explorations into the Effects of Learning on the Rate of Evolution."

Connected through hyperedge HE-2 (induced by source paper "Genes, Phenes and the Baldwin Effect: Learning and Evolution in a Simulated Population"):
  with members:
    - "Culling Teaching -1 Culling and Teaching in Neuro-evolution"
    - "On the relationship between distributed group-behaviour and the behavioural complexity of individuals [Wilson Sober 1994])"
    - "Artificial Life as Theoretical Biology: How to do real science with computer simulation"

Connected through hyperedge HE-3 (induced by source paper "EVOLVING SENSORS IN ENVIRONMENTS OF CONTROLLED COMPLEXITY"):
  with members:
    - "Environmental Effects on Minimal Behaviors in the Minimat World"
    - "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