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: "Adapting Crossover in a Genetic Algorithm"
  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. Despite theoretical analysis, however, it appears difficult to predict when a particular crossover form will be optimal for a given problem. This paper describes an adaptive genetic algorithm that decides, as it runs, which form is optimal.

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"
    - "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?"
    - "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 "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-3 (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"
    - "A Survey of Evolution Strategies"
    - "An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms"

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