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: "A STUDY OF CROSSOVER OPERATORS IN GENETIC PROGRAMMING"
  Abstract: Holland's analysis of the sources of power of genetic algorithms has served as guidance for the applications of genetic algorithms for more than 15 years. The technique of applying a recombination operator (crossover) to a population of individuals is a key to that power. Neverless, there have been a number of contradictory results concerning crossover operators with respect to overall performance. Recently, for example, genetic algorithms were used to design neural network modules and their control circuits. In these studies, a genetic algorithm without crossover outperformed a genetic algorithm with crossover. This report re-examines these studies, and concludes that the results were caused by a small population size. New results are presented that illustrate the effectiveness of crossover when the population size is larger. From a performance view, the results indicate that better neural networks can be evolved in a shorter time if the genetic algorithm uses crossover.

Connected through hyperedge HE-1 (induced by source paper "Crossover or Mutation?"):
  with members:
    - "Generation Gaps Revisited"
    - "A Survey of Evolution Strategies"
    - "An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms"
    - "On the Virtues of Parameterized Uniform Crossover"

Connected through hyperedge HE-2 (induced by source paper "An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms"):
  with members:
    - "On The State of Evolutionary Computation"
    - "Adapting Crossover in a Genetic Algorithm"
    - "Distribution Category:  A Parallel Genetic Algorithm for the Set Partitioning Problem"
    - "Using Problem Generators to Explore the Effects of Epistasis"
    - "On the Virtues of Parameterized Uniform Crossover"
    - "Crossover or Mutation?"
    - "Raising GA Performance by Simultaneous Tuning of Selective Pressure and Recombination Disruptiveness"

Connected through hyperedge HE-3 (induced by source paper "On the Virtues of Parameterized Uniform Crossover"):
  with members:
    - "Recombination Operator, its Correlation to the Fitness Landscape and Search Performance"
    - "Distribution Category:  A Parallel Genetic Algorithm for the Set Partitioning Problem"
    - "An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms"
    - "Crossover or Mutation?"

Connected through hyperedge HE-4 (induced by source paper "Genetic Algorithms For Vertex Splitting in DAGs 1"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."

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