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: "On the Virtues of Parameterized Uniform Crossover"
  Abstract: Traditionally, genetic algorithms have relied upon 1 and 2-point crossover operators. Many recent empirical studies, however, have shown the benefits of higher numbers of crossover points. Some of the most intriguing recent work has focused on uniform crossover, which involves on the average L/2 crossover points for strings of length L. Theoretical results suggest that, from the view of hyperplane sampling disruption, uniform crossover has few redeeming features. However, a growing body of experimental evidence suggests otherwise. In this paper, we attempt to reconcile these opposing views of uniform crossover and present a framework for understanding its virtues.

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"
    - "A STUDY OF CROSSOVER OPERATORS IN GENETIC PROGRAMMING"

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"
    - "Crossover or Mutation?"
    - "A STUDY OF CROSSOVER OPERATORS IN GENETIC PROGRAMMING"
    - "Raising GA Performance by Simultaneous Tuning of Selective Pressure and Recombination Disruptiveness"

Connected through hyperedge HE-3 (induced by source paper "Recombination Operator, its Correlation to the Fitness Landscape and Search Performance"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Evolution in Time and Space The Parallel Genetic Algorithm"
    - "A Survey of Evolution Strategies"
    - "Genetic Programming of Minimal Neural Nets Using Occam's Razor"

Connected through hyperedge HE-4 (induced by source paper "A STUDY OF CROSSOVER OPERATORS IN GENETIC PROGRAMMING"):
  with members:
    - "Genetic Algorithms For Vertex Splitting in DAGs 1"
    - "An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms"
    - "Crossover or Mutation?"

Connected through hyperedge HE-5 (induced by source paper "Distribution Category:  A Parallel Genetic Algorithm for the Set Partitioning Problem"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Surgery"
    - "A Comparative Study of Genetic Search"
    - "A Survey of Parallel Genetic Algorithms"
    - "Scheduling Maintenance of Electrical Power Transmission Networks Using Genetic Programming"
    - "Genetic Algorithms for Combinatorial Optimization: The Assembly Line Balancing Problem"
    - "An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms"
    - "As mentioned in the introduction, an encod-ing/crossover pair makes a spectrum of geographical linkages. A"

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