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: "Local Selection"
  Abstract: Local selection (LS) is a very simple selection scheme in evolutionary algorithms. Individual fitnesses are compared to a fixed threshold, rather than to each other, to decide who gets to reproduce. LS, coupled with fitness functions stemming from the consumption of shared environmental resources, maintains diversity in a way similar to fitness sharing; however it is generally more efficient than fitness sharing, and lends itself to parallel implementations for distributed tasks. While LS is not prone to premature convergence, it applies minimal selection pressure upon the population. LS is therefore more appropriate than other, stronger selection schemes only on certain problem classes. This papers characterizes one broad class of problems in which LS consistently out performs tournament selection.

Connected through hyperedge HE-1 (induced by source paper "Complex Environments to Complex Behaviors  FROM COMPLEX ENVIRONMENTS TO COMPLEX BEHAVIORS"):
  with members:
    - "Environmental Effects on Minimal Behaviors in the Minimat World"

Connected through hyperedge HE-2 (induced by source paper "An Analysis of the Effects of Neighborhood Size and Shape on Local Selection Algorithms"):
  with members:
    - "Using Neural Networks and Genetic Algorithms as Heuristics for NP-Complete Problems"
    - "A Survey of Parallel Genetic Algorithms"
    - "Evolution in Time and Space The Parallel Genetic Algorithm"

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