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: "Evolving Compact Solutions in Genetic Programming: A Case Study"
  Abstract: Genetic programming (GP) is a variant of genetic algorithms where the data structures handled are trees. This makes GP especially useful for evolving functional relationships or computer programs, as both can be represented as trees. Symbolic regression is the determination of a function dependence y = g(x) that approximates a set of data points (x i ; y i ). In this paper the feasibility of symbolic regression with GP is demonstrated on two examples taken from different domains. Furthermore several suggested methods from literature are compared that are intended to improve GP performance and the readability of solutions by taking into account introns or redundancy that occurs in the trees and keeping the size of the trees small. The experiments show that GP is an elegant and useful tool to derive complex functional dependencies on numerical data.

Connected through hyperedge HE-1 (induced by source paper "A Comparison of Selection Schemes used in Genetic Algorithms"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Determining Successful Negotiation Strategies: An Evolutionary Approach"
    - "Overview of Selection Schemes and a Suggested Classification"
    - "Tackling the Boolean Even N Parity Problem with Genetic Programming and Limited-Error Fitness standard GP"
    - "Genetic Programming and Redundancy"

Connected through hyperedge HE-2 (induced by source paper "Genetic Programming and Redundancy"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "A Comparison of Selection Schemes used in Genetic Algorithms"
    - "Causality in Genetic Programming"
    - "Fitness Landscapes and Difficulty in Genetic Programming"
    - "Position Paper, Workshop on Evolutionary Computation with Variable Size Representation, ICGA, Fitness Causes Bloat in"
    - "An Adverse Interaction between the Crossover Operator and a Restriction on Tree Depth of Crossover"
    - "The MAX Problem for Genetic Programming Highlighting an Adverse Interaction between the Crossover Operator and"

Connected through hyperedge HE-3 (induced by source paper "Complexity Compression and Evolution"):
  with members:
    - "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 Study in Program Response and the Negative Effects of Introns in Genetic Programming"
    - "A Survey of Intron Research in Genetics"

Connected through hyperedge HE-4 (induced by source paper "Balancing Accuracy and Parsimony in Genetic Programming 1"):
  with members:
    - "Generality versus Size in Genetic Programming"

Connected through hyperedge HE-5 (induced by source paper "Causality in Genetic Programming"):
  with members:
    - "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"
    - "A Study in Program Response and the Negative Effects of Introns 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