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 Crossover and Mutation in Genetic Programming"
  Abstract: This paper presents a large and systematic body of data on the relative effectiveness of mutation, crossover, and combinations of mutation and crossover in genetic programming (GP). The literature of traditional genetic algorithms contains related studies, but mutation and crossover in GP differ from their traditional counterparts in significant ways. In this paper we present the results from a very large experimental data set, the equivalent of approximately 12,000 typical runs of a GP system, systematically exploring a range of parameter settings. The resulting data may be useful not only for practitioners seeking to optimize parameters for GP runs, but also for theorists exploring issues such as the role of building blocks in GP.

Connected through hyperedge HE-1 (induced by source paper "The Impact of External Dependency in Genetic Programming Primitives"):
  with members:
    - "The Troubling Aspects of a Building Block Hypothesis for Genetic Programming"
    - "The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance"
    - "How Fitness Structure Affects Subsolution Acquisition in Genetic Programming"

Connected through hyperedge HE-2 (induced by source paper "The Automatic Programming of Agents that Learn Mental Models and Create Simple Plans of Action"):
  with members:
    - "Evolution of Iteration in Genetic Programming D a v d A The solution to many"
    - "Analysis of Neurocontrollers Designed by Simulated Evolution"
    - "20 Data Structures and Genetic Programming  two techniques for reducing run time."
    - "Evolution of Mapmaking: Learning, planning, and memory using Genetic Programming"
    - "Cultural Transmission of Information in Genetic Programming"
    - "Evolving Teamwork and Coordination with Genetic Programming"
    - "Culture Enhances the Evolvability of Cognition"
    - "Neural Programming and an Internal Reinforcement Policy"

Connected through hyperedge HE-3 (induced by source paper "The Troubling Aspects of a Building Block Hypothesis for Genetic Programming"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Genetic Programming Exploratory Power and the Discovery of Functions"
    - "Program Search with a Hierarchical Variable Length Representation: Genetic Programming, Simulated Annealing and Hill Climbing"
    - "The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance"
    - "Towards Automatic Discovery of Building Blocks in Genetic Programming"
    - "The Schema Theorem and Price's Theorem"
    - "An Experimental Analysis of Schema Creation, Propagation and Disruption in Genetic Programming"
    - "An Analysis of the MAX Problem in Genetic Programming hold only in some cases, in"

Connected through hyperedge HE-4 (induced by source paper "A Study in Program Response and the Negative Effects of Introns in Genetic Programming"):
  with members:
    - "Causality in Genetic Programming"
    - "Complexity Compression and Evolution"

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