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: "Optimal and Asymptotically Optimal Equi-partition of Rectangular Domains via Stripe Decomposition"
  Abstract: We present an efficient method for assigning any number of processors to tasks associated with the cells of a rectangular uniform grid. Load balancing equi-partition constraints are observed while approximately minimizing the total perimeter of the partition, which corresponds to the amount of interprocessor communication. This method is based upon decomposition of the grid into stripes of "optimal" height. We prove that under some mild assumptions, as the problem size grows large in all parameters, the error bound associated with this feasible solution approaches zero. We also present computational results from a high level parallel Genetic Algorithm that utilizes this method, and make comparisons with other methods. On a network of workstations, our algorithm solves within minutes instances of the problem that would require one billion binary variables in a Quadratic Assignment formulation.

Connected through hyperedge HE-1 (induced by source paper "Fast EquiPartitioning of Rectangular Domains using Stripe Decomposition"):
  with members:
    - "Genetic Algorithms as Multi-Coordinators in Large-Scale Optimization"

Connected through hyperedge HE-2 (induced by source paper "Minimum-Perimeter Domain Assignment"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "Genetic Algorithms as Multi-Coordinators in Large-Scale Optimization"):
  with members:
    - "A Cooperative Coevolutionary Approach to Function Optimization"
    - "Fast EquiPartitioning of Rectangular Domains using Stripe Decomposition"

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