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 Study in Program Response and the Negative Effects of Introns in Genetic Programming"
  Abstract: The standard method of obtaining a response in tree-based genetic programming is to take the value returned by the root node. In non-tree representations, alternate methods have been explored. One alternative is to treat a specific location in indexed memory as the response value when the program terminates. The purpose of this paper is to explore the applicability of this technique to tree-structured programs and to explore the intron effects that these studies bring to light. This paper's experimental results support the finding that this memory-based program response technique is an improvement for some, but not all, problems. In addition, this paper's experimental results support the finding that, contrary to past research and speculation, the addition or even facilitation of introns can seriously degrade the search performance of genetic programming.

Connected through hyperedge HE-1 (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 Survey of Intron Research in Genetics"

Connected through hyperedge HE-2 (induced by source paper "A Comparison of Crossover and Mutation in Genetic Programming"):
  with members:
    - "The Troubling Aspects of a Building Block Hypothesis for Genetic Programming"
    - "The Automatic Programming of Agents that Learn Mental Models and Create Simple Plans of Action"
    - "The Impact of External Dependency in Genetic Programming Primitives"

Connected through hyperedge HE-3 (induced by source paper "Causality in Genetic Programming"):
  with members:
    - "Evolving Compact Solutions in Genetic Programming: A Case Study"
    - "Genetic Programming and Redundancy"
    - "Genetic Programming Exploratory Power and the Discovery of Functions"
    - "Hierarchical Self-Organization in Genetic Programming"
    - "Towards Automatic Discovery of Building Blocks in Genetic Programming"
    - "Position Paper, Workshop on Evolutionary Computation with Variable Size Representation, ICGA, Fitness Causes Bloat in"

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