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: "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.

Connected through hyperedge HE-1 (induced by source paper "Fast EquiPartitioning of Rectangular Domains using Stripe Decomposition"):
  with members:
    - "Optimal and Asymptotically Optimal Equi-partition of Rectangular Domains via Stripe Decomposition"

Connected through hyperedge HE-2 (induced by source paper "Optimal and Asymptotically Optimal Equi-partition of Rectangular Domains via Stripe Decomposition"):
  with members:
    - "Fast EquiPartitioning of Rectangular Domains using Stripe Decomposition"
    - "Minimum-Perimeter Domain Assignment"

Connected through hyperedge HE-3 (induced by source paper "A Cooperative Coevolutionary Approach to Function Optimization"):
  with members:
    - "Performance of Multi-Parent Crossover Operators on Numerical Function Optimization Problems"
    - "Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms"
    - "EVOLVING NEURAL NETWORKS WITH COLLABORATIVE SPECIES"
    - "A Coevolutionary Approach to Learning Sequential Decision Rules"
    - "Evolving Complex Structures via Co- operative Coevolution"

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