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: "Modeling Distributed Search via Social Insects"
  Abstract: Complex group behavior arises in social insects colonies as the integration of the actions of simple and redundant individual insects [Adler and Gordon, 1992, Oster and Wilson, 1978]. Furthermore, the colony can act as an information center to expedite foraging [Brown, 1989]. We apply these lessons from natural systems to model collective action and memory in a computational agent society. Collective action can expedite search in combinatorial optimization problems [Dorigo et al., 1996]. Collective memory can improve learning in multi-agent systems [Garland and Alterman, 1996]. Our collective adaptation integrates the simplicity of collective action with the pattern detection of collective memory to significantly improve both the gathering and processing of knowledge. As a test of the role of the society as an information center, we examine the ability of the society to distribute task allocation without any omnipotent centralized control.

Connected through hyperedge HE-1 (induced by source paper "Type Inheritance in Strongly Typed Genetic Programming"):
  with members:
    - "Strongly Typed Genetic Programming"
    - "A Comparison of Random Search versus Genetic Programming as Engines for Collective Adaptation"
    - "Entailment for Specification Refinement"
    - "Augmenting Collective Adaptation with Simple Process Agents"
    - "ABSTRACT"
    - "Clique Detection via Genetic Programming  Topics in Combinatorial Optimization"

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "Strongly Typed Genetic Programming"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Towards Automatic Discovery of Building Blocks in Genetic Programming"
    - "A Methodology for Processing Problem Constraints in Genetic Programming"
    - "A Simulation of Adaptive Agents in a Hostile Environment"
    - "A Comparison of Random Search versus Genetic Programming as Engines for Collective Adaptation"
    - "Type Inheritance in Strongly Typed Genetic Programming"
    - "1 GP-COM: A Distributed, Component-Based Genetic Programming System in C++"
    - "Program Optimization for Faster 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