human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Genetic algorithms with multi-parent recombination"):
  Abstract: In this paper we investigate genetic algorithms where more than two parents are involved in the recombination operation. In particular, we introduce gene scanning as a reproduction mechanism that generalizes classical crossovers, such as n-point crossover or uniform crossover, and is applicable to an arbitrary number (two or more) of parents. We performed extensive tests for optimizing numerical functions, the TSP and graph coloring to observe the effect of different numbers of parents. The experiments show that 2-parent recombination is outperformed when using more parents on the classical DeJong functions. For the other problems the results are not conclusive, in some cases 2 parents are optimal, while in some others more parents are better.

Includes members:
  Member "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Evolving Graphs and Networks with Edge Encoding: Preliminary Report")
  Member "LIBGA: A USER-FRIENDLY WORKBENCH FOR ORDER-BASED GENETIC ALGORITHM RESEARCH":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Heuristic for Improved Genetic Bin Packing")
  Member "Performance of Multi-Parent Crossover Operators on Numerical Function Optimization Problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Multi-parent's niche: n-ary crossovers on NK-landscapes")
  Member "Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.")
  Member "Multi-parent Recombination":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms")
  Member "Graph Coloring with Adaptive Evolutionary Algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Solving 3-SAT by GAs Adapting Constraint Weights")
  Member "Solving 3-SAT by GAs Adapting Constraint Weights":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "Graph Coloring with Adaptive Evolutionary Algorithms")
  Member "Raising GA Performance by Simultaneous Tuning of Selective Pressure and Recombination Disruptiveness":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "An Analysis of the Interacting Roles of Population Size and Crossover in Genetic Algorithms")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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