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: "Nonlinearity, Hyperplane Ranking and the Simple Genetic Algorithm"
  Abstract: Several metrics are used in empirical studies to explore the mechanisms of convergence of genetic algorithms. The metric is designed to measure the consistency of an arbitrary ranking of hyperplanes in a partition with respect to a target string. Walsh coefficients can be calculated for small functions in order to characterize sources of linear and nonlinear interactions. A simple deception measure is also developed to look closely at the effects of increasing nonlinearity of functions. Correlations between the metric and deception measure are discussed and relationships between and convergence behavior of a simple genetic algorithm are studied over large sets of functions with varying degrees of nonlinearity.

Connected through hyperedge HE-1 (induced by source paper "3 Representation Issues in Neighborhood Search and Evolutionary Algorithms"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Evaluating Evolutionary Algorithms"
    - "Hyperplane Ranking in Simple Genetic Algorithms"

Connected through hyperedge HE-2 (induced by source paper "Hyperplane Ranking in Simple Genetic Algorithms"):
  with members:
    - "3 Representation Issues in Neighborhood Search and Evolutionary Algorithms"

Connected through hyperedge HE-3 (induced by source paper "Walsh Functions and Predicting Problem Complexity"):
  with members:
    (none)

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