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: "Testing the Robustness of the Genetic Algorithm on the Floating Building Block Representation."
  Abstract: Recent studies on a floating building block representation for the genetic algorithm (GA) suggest that there are many advantages to using the floating representation. This paper investigates the behavior of the GA on floating representation problems in response to three different types of pressures: (1) a reduction in the amount of genetic material available to the GA during the problem solving process, (2) functions which have negative-valued building blocks, and (3) randomizing non-coding segments. Results indicate that the GA's performance on floating representation problems is very robust. Significant reductions in genetic material (genome length) may be made with relatively small decrease in performance. The GA can effectively solve problems with negative building blocks. Randomizing non-coding segments appears to improve rather than harm GA performance.

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"
    - "A comparison of the fixed and floating building block representation in the genetic algorithm"
    - "The Impact of External Dependency in Genetic Programming Primitives"

Connected through hyperedge HE-2 (induced by source paper "A comparison of the fixed and floating building block representation in the genetic algorithm"):
  with members:
    - "The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance"
    - "Empirical studies of the genetic algorithm with non-coding segments"
    - "Duplication of Coding Segments in Genetic Programming"
    - "A Survey of Intron Research in Genetics"

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