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: "The Evolutionary Cost of Learning"
  Abstract: Traits that are acquired by members of an evolving population during their lifetime, through adaptive processes such as learning, can become genetically specified in later generations. Thus there is a change in the level of learning in the population over evolutionary time. This paper explores the idea that as well as the benefits to be gained from learning, there may also be costs to be paid for the ability to learn. It is these costs that supply the selection pressure for the genetic assimilation of acquired traits. Two models are presented that attempt to illustrate this assertion. The first uses Kauffman's NK fitness landscapes to show the effect that both explicit and implicit costs have on the assimilation of learnt traits. A characteristic `hump' is observed in the graph of the level of plasticity in the population showing that learning is first selected for and then against as evolution progresses. The second model is a practical example in which neural network controllers are evolved for a small mobile robot. Results from this experiment also show the hump.

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 "Landscapes, Learning Costs and Genetic Assimilation."):
  with members:
    - "Genes, Phenes and the Baldwin Effect: Learning and Evolution in a Simulated Population"
    - "EVOLVING SENSORS IN ENVIRONMENTS OF CONTROLLED COMPLEXITY"

Connected through hyperedge HE-3 (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"
    - "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"
    - "Environmental Effects on Minimal Behaviors in the Minimat World"
    - "Evolving nonTrivial Behaviors on Real Robots: an Autonomous Robot that Picks up Objects"
    - "Evolving Visually Guided Robots"

Connected through hyperedge HE-4 (induced by source paper "Guiding or Hiding: Explorations into the Effects of Learning on the Rate of Evolution."):
  with members:
    - "EVOLVING SENSORS IN ENVIRONMENTS OF CONTROLLED COMPLEXITY"

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