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 Recombination"
  Abstract: In this section we survey recombination operators that can apply more than two parents to create offspring. Some multi-parent recombination operators are defined for a fixed number of parents, e.g. have arity three, in some operators the number of parents is a random number that might be greater than two, and in yet other operators the arity is a parameter that can be set to an arbitrary integer number. We pay special attention to this latter type of operators and summarize results on the effect of operator arity on EA performance.

Connected through hyperedge HE-1 (induced by source paper "Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Genetic algorithms with multi-parent recombination"
    - "A Cooperative Coevolutionary Approach to Function Optimization"
    - "Graph Coloring with Adaptive Evolutionary Algorithms"

Connected through hyperedge HE-2 (induced by source paper "Adapting Crossover in Evolutionary Algorithms"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "Evolutionary Programming and Evolution Strategies: Similarities and Differences"):
  with members:
    - "An Analysis of the MAX Problem in Genetic Programming hold only in some cases, in"

Connected through hyperedge HE-4 (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"
    - "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"
    - "Multi-parent's niche: n-ary crossovers on NK-landscapes"

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