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: "Multi-parent's niche: n-ary crossovers on NK-landscapes"
  Abstract: Using the multi-parent diagonal and scanning crossover in GAs reproduction operators obtain an adjustable arity. Hereby sexuality becomes a graded feature instead of a Boolean one. Our main objective is to relate the performance of GAs to the extent of sexuality used for reproduction on less arbitrary functions then those reported in the current literature. We investigate GA behaviour on Kauffman's NK-landscapes that allow for systematic characterization and user control of ruggedness of the fitness landscape. We test GAs with a varying extent of sexuality, ranging from asexual to 'very sexual'. Our tests were performed on two types of NK-landscapes: landscapes with random and landscapes with nearest neighbour epistasis. For both landscape types we selected landscapes from a range of ruggednesses. The results confirm the superiority of (very) sexual recombination on mildly epistatic problems.

Connected through hyperedge HE-1 (induced by source paper "On the Effectiveness of Evolutionary Search in High-Dimensional NK-Landscapes"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Using Problem Generators to Explore the Effects of Epistasis"
    - "A Genetic Local Search Approach to the Quadratic Assignment Problem"

Connected through hyperedge HE-2 (induced by source paper "Performance of Multi-Parent Crossover Operators on Numerical Function Optimization Problems"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "LIBGA: A USER-FRIENDLY WORKBENCH FOR ORDER-BASED GENETIC ALGORITHM RESEARCH"
    - "Genetic algorithms with multi-parent recombination"
    - "A Cooperative Coevolutionary Approach to Function Optimization"

Connected through hyperedge HE-3 (induced by source paper "Genetic algorithms with multi-parent recombination"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "LIBGA: A USER-FRIENDLY WORKBENCH FOR ORDER-BASED GENETIC ALGORITHM RESEARCH"
    - "Performance of Multi-Parent Crossover Operators on Numerical Function Optimization Problems"
    - "Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms"
    - "Multi-parent Recombination"
    - "Graph Coloring with Adaptive Evolutionary Algorithms"
    - "Solving 3-SAT by GAs Adapting Constraint Weights"
    - "Raising GA Performance by Simultaneous Tuning of Selective Pressure and Recombination Disruptiveness"

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