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 Survey of Intron Research in Genetics"
  Abstract: A brief survey of biological research on non-coding DNA is presented here. There has been growing interest in the effects of non-coding segments in evolutionary algorithms (EAs). To better understand and conduct research on non-coding segments and EAs, it is important to understand the biological background of such work. This paper begins with a review of basic genetics and terminology, describes the different types of non-coding DNA, and then surveys recent intron research.

Connected through hyperedge HE-1 (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 comparison of the fixed and floating building block representation in the genetic algorithm"

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"
    - "Testing the Robustness of the Genetic Algorithm on the Floating Building Block Representation."

Connected through hyperedge HE-3 (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"
    - "Modeling Distributed Search via Social Insects"
    - "Entailment for Specification Refinement"
    - "Augmenting Collective Adaptation with Simple Process Agents"
    - "A comparison of the fixed and floating building block representation in the genetic algorithm"

Connected through hyperedge HE-4 (induced by source paper "Evolving Turing-Complete Programs for a Register Machine with Self-modifying Code"):
  with members:
    - "A Genome Compiler for High Performance Genetic Programming"

Connected through hyperedge HE-5 (induced by source paper "Complexity Compression and Evolution"):
  with members:
    - "Evolving Compact Solutions in Genetic Programming: A Case Study"
    - "Signal Path Oriented Approach for Generation of Dynamic Process Models"
    - "Fitness Landscapes and Difficulty in Genetic Programming"
    - "Culling Teaching -1 Culling and Teaching in Neuro-evolution"
    - "A Study in Program Response and the Negative Effects of Introns in Genetic Programming"

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