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: "An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms"
  Abstract: In this paper we present some theoretical and empirical results on the interacting roles of population size and crossover in genetic algorithms. We summarize recent theoretical results on the disruptive effect of two forms of multi-point crossover: n-point crossover and uniform crossover. We then show empirically that disruption analysis alone is not sufficient for selecting appropriate forms of crossover. However, by taking into account the interacting effects of population size and crossover, a general picture begins to emerge. The implications of these results on implementation issues and performance are discussed, and several directions for further research are suggested.

Connected through hyperedge HE-1 (induced by source paper "Crossover or Mutation?"):
  with members:
    - "Generation Gaps Revisited"
    - "A Survey of Evolution Strategies"
    - "On the Virtues of Parameterized Uniform Crossover"
    - "A STUDY OF CROSSOVER OPERATORS IN GENETIC PROGRAMMING"

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"
    - "Crossover or Mutation?"
    - "A STUDY OF CROSSOVER OPERATORS IN GENETIC PROGRAMMING"

Connected through hyperedge HE-3 (induced by source paper "Adapting Crossover in a Genetic Algorithm"):
  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"

Connected through hyperedge HE-4 (induced by source paper "On The State of Evolutionary Computation"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Dynamic Parameter Encoding for Genetic Algorithms"
    - "Adapting Crossover in a Genetic Algorithm"
    - "A Survey of Evolution Strategies"

Connected through hyperedge HE-5 (induced by source paper "A STUDY OF CROSSOVER OPERATORS IN GENETIC PROGRAMMING"):
  with members:
    - "Genetic Algorithms For Vertex Splitting in DAGs 1"
    - "On the Virtues of Parameterized Uniform Crossover"
    - "Crossover or Mutation?"

Connected through hyperedge HE-6 (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"
    - "On the Virtues of Parameterized Uniform Crossover"
    - "As mentioned in the introduction, an encod-ing/crossover pair makes a spectrum of geographical linkages. A"

Connected through hyperedge HE-7 (induced by source paper "Raising GA Performance by Simultaneous Tuning of Selective Pressure and Recombination Disruptiveness"):
  with members:
    - "Genetic algorithms with multi-parent recombination"

Connected through hyperedge HE-8 (induced by source paper "Using Problem Generators to Explore the Effects of Epistasis"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "On the Effectiveness of Evolutionary Search in High-Dimensional NK-Landscapes"
    - "Using Neural Networks and Genetic Algorithms as Heuristics for NP-Complete Problems"

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