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: "Augmenting Collective Adaptation with Simple Process Agents"
  Abstract: We have integrated the distributed search of genetic programming based systems with collective memory to form a collective adaptation search method. Such a system significantly improves search as problem complexity is increased. However, there is still considerable scope for improvement. In collective adaptation, search agents gather knowledge of their environment and deposit it in a central information repository. Process agents are then able to manipulate that focused knowledge, exploiting the exploration of the search agents. We examine the utility of increasing the capabilities of the centralized process agents.

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"
    - "Modeling Distributed Search via Social Insects"
    - "Entailment for Specification Refinement"
    - "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"
    - "Modeling Distributed Search via Social Insects"
    - "Entailment for Specification Refinement"
    - "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"
    - "Modeling Distributed Search via Social Insects"
    - "1 GP-COM: A Distributed, Component-Based Genetic Programming System in C++"

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