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: "A comparison of the fixed and floating building block representation in the genetic algorithm"
  Abstract: This article compares the traditional, fixed problem representation style of a genetic algorithm (GA) with a new floating representation in which the building blocks of a problem are not fixed at specific locations on the individuals of the population. In addition, the effects of non-coding segments on both of these representations is studied. Non-coding segments are a computational model of non-coding DNA and floating building blocks mimic the location independence of genes. The fact that these structures are prevalent in natural genetic systems suggests that they may provide some advantages to the evolutionary process. Our results show that there is a significant difference in how GAs solve a problem in the fixed and floating representations. GAs are able to maintain a more diverse population with the floating representation. The combination of non-coding segments and floating building blocks appears to encourage a GA to take advantage of its parallel search and recombination abilities.

Connected through hyperedge HE-1 (induced by source paper "The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "The Troubling Aspects of a Building Block Hypothesis for Genetic Programming"
    - "Distributed Collective Adaptation Applied to a Hard Combinatorial Optimization Problem"
    - "Modeling Building-Block Interdependency  Dynamical and Evolutionary Machine Organization Group"
    - "Voting for Schemata"
    - "Using Genetic Algorithms to Explore Pattern Recognition in the Immune System  COMMENTS WELCOME"
    - "Testing the Robustness of the Genetic Algorithm on the Floating Building Block Representation."
    - "The Impact of External Dependency in Genetic Programming Primitives"

Connected through hyperedge HE-2 (induced by source paper "Testing the Robustness of the Genetic Algorithm on the Floating Building Block Representation."):
  with members:
    - "The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance"

Connected through hyperedge HE-3 (induced by source paper "Empirical studies of the genetic algorithm with non-coding segments"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Dynamic Control of Genetic Algorithms using Fuzzy Logic Techniques"
    - "A Survey of Intron Research in Genetics"

Connected through hyperedge HE-4 (induced by source paper "Duplication of Coding Segments in Genetic Programming"):
  with members:
    - "A Comparison of Random Search versus Genetic Programming as Engines for Collective Adaptation"
    - "A Survey of Intron Research in Genetics"
    - "Modeling Distributed Search via Social Insects"
    - "Entailment for Specification Refinement"
    - "Augmenting Collective Adaptation with Simple Process Agents"

Connected through hyperedge HE-5 (induced by source paper "A Survey of Intron Research in Genetics"):
  with members:
    - "Empirical studies of the genetic algorithm with non-coding segments"
    - "Complexity Compression and Evolution"
    - "Duplication of Coding Segments in Genetic Programming"
    - "Evolving Turing-Complete Programs for a Register Machine with Self-modifying Code"

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