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 as Multi-Coordinators in Large-Scale Optimization"):
  Abstract: We present high-level, decomposition-based algorithms for large-scale block-angular optimization problems containing integer variables, and demonstrate their effectiveness in the solution of large-scale graph partitioning problems. These algorithms combine the subproblem-coordination paradigm (and lower bounds) of price-directive decomposition methods with knapsack and genetic approaches to the utilization of "building blocks" of partial solutions. Even for graph partitioning problems requiring billions of variables in a standard 0-1 formulation, this approach produces high-quality solutions (as measured by deviations from an easily computed lower bound), and substantially outperforms widely-used graph partitioning techniques based on heuristics and spectral methods.

Includes members:
  Member "A Cooperative Coevolutionary Approach to Function Optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms")
  Member "Optimal and Asymptotically Optimal Equi-partition of Rectangular Domains via Stripe Decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Fast EquiPartitioning of Rectangular Domains using Stripe Decomposition")
  Member "Fast EquiPartitioning of Rectangular Domains using Stripe Decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Optimal and Asymptotically Optimal Equi-partition of Rectangular Domains via Stripe Decomposition")

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