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: "An Analysis of the Effects of Neighborhood Size and Shape on Local Selection Algorithms"
  Abstract: The increasing availability of finely-grained parallel architectures has resulted in a variety of evolutionary algorithms (EAs) in which the population is spatially distributed and local selection algorithms operate in parallel on small, overlapping neighborhoods. The effects of design choices regarding the particular type of local selection algorithm as well as the size and shape of the neighborhood are not particularly well understood and are generally tested empirically. In this paper we extend the techniques used to more formally analyze selection methods for sequential EAs and apply them to local neighborhood models, resulting in a much clearer understanding of the effects of neighborhood size and shape.

Connected through hyperedge HE-1 (induced by source paper "Evolution in Time and Space The Parallel Genetic Algorithm"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Island Model Genetic Algorithms and Linearly Separable Problems"
    - "A Study of Genetic Algorithms to Find Approximate Solutions to Hard 3CNF Problems"
    - "Recombination Operator, its Correlation to the Fitness Landscape and Search Performance"
    - "Genetic Algorithm Programming Environments"
    - "A Survey of Parallel Genetic Algorithms"
    - "Staged Hybrid Genetic Search for Seismic Data Imaging"
    - "The Schema Theorem and Price's Theorem"

Connected through hyperedge HE-2 (induced by source paper "Local Selection"):
  with members:
    - "Complex Environments to Complex Behaviors  FROM COMPLEX ENVIRONMENTS TO COMPLEX BEHAVIORS"

Connected through hyperedge HE-3 (induced by source paper "A Survey of Parallel Genetic Algorithms"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Distribution Category:  A Parallel Genetic Algorithm for the Set Partitioning Problem"
    - "Evolution in Time and Space The Parallel Genetic Algorithm"
    - "Genetic Algorithms for Combinatorial Optimization: The Assembly Line Balancing Problem"
    - "Speeding up Genetic Programming: A Parallel BSP implementation the Bulk Synchronous Parallel Pro gramming (BSP)"

Connected through hyperedge HE-4 (induced by source paper "Using Neural Networks and Genetic Algorithms as Heuristics for NP-Complete Problems"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "HGA: A Hardware-Based Genetic Algorithm"
    - "Vector Quantizer Design Using Genetic Algorithms"
    - "A Comparative Study of Genetic Search"
    - "Using Problem Generators to Explore the Effects of Epistasis"
    - "How good are genetic algorithms at finding large cliques: an experimental study"
    - "An Evolutionary Approach to Vector Quantizer Design"
    - "Using Genetic Algorithms for Supervised Concept Learning"

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