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 Random Search versus Genetic Programming as Engines for Collective Adaptation"
  Abstract: We have integrated the distributed search of genetic programming (GP) based systems with collective memory to form a collective adaptation search method. Such a system significantly improves search as problem complexity is increased. Since the pure GP approach does not scale well with problem complexity, a natural question is which of the two components is actually contributing to the search process. We investigate a collective memory search which utilizes a random search engine and find that it significantly outperforms the GP based search engine. We examine the solution space and show that as problem complexity and search space grow, a collective adaptive system will perform better than a collective memory search employing random search as an engine.

Connected through hyperedge HE-1 (induced by source paper "Type Inheritance in Strongly Typed Genetic Programming"):
  with members:
    - "Strongly Typed Genetic Programming"
    - "Modeling Distributed Search via Social Insects"
    - "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 Survey of Intron Research in Genetics"
    - "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-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"
    - "Type Inheritance in Strongly Typed Genetic Programming"
    - "Modeling Distributed Search via Social Insects"
    - "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