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: "Crossover or Mutation?"
  Abstract: Genetic algorithms rely on two genetic operators crossover and mutation. Although there exists a large body of conventional wisdom concerning the roles of crossover and mutation, these roles have not been captured in a theoretical fashion. For example, it has never been theoretically shown that mutation is in some sense "less powerful" than crossover or vice versa. This paper provides some answers to these questions by theoretically demonstrating that there are some important characteristics of each operator that are not captured by the other.

Connected through hyperedge HE-1 (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"
    - "A STUDY OF CROSSOVER OPERATORS IN GENETIC PROGRAMMING"
    - "Raising GA Performance by Simultaneous Tuning of Selective Pressure and Recombination Disruptiveness"

Connected through hyperedge HE-2 (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"
    - "A STUDY OF CROSSOVER OPERATORS IN GENETIC PROGRAMMING"

Connected through hyperedge HE-3 (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"
    - "On the Virtues of Parameterized Uniform Crossover"

Connected through hyperedge HE-4 (induced by source paper "A Survey of Evolution Strategies"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "On The State of Evolutionary Computation"
    - "A Stochastic Search Approach to Grammar Induction"
    - "Recombination Operator, its Correlation to the Fitness Landscape and Search Performance"
    - "Evaluating Evolutionary Algorithms"
    - "Optimal Mutation Rates in Genetic Search"
    - "Extended Selection Mechanisms in Genetic Algorithms"
    - "Evolutionary Algorithms: Some Very Old Strategies for Optimization and Adaptation"

Connected through hyperedge HE-5 (induced by source paper "Generation Gaps Revisited"):
  with members:
    - "LIBGA: A USER-FRIENDLY WORKBENCH FOR ORDER-BASED GENETIC ALGORITHM RESEARCH"

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