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: "Genetic Programming Exploratory Power and the Discovery of Functions"
  Abstract: Hierarchical genetic programming (HGP) approaches rely on the discovery, modification, and use of new functions to accelerate evolution. This paper provides a qualitative explanation of the improved behavior of HGP, based on an analysis of the evolution process from the dual perspective of diversity and causality. From a static point of view, the use of an HGP approach enables the manipulation of a population of higher diversity programs. Higher diversity increases the exploratory ability of the genetic search process, as demonstrated by theoretical and experimental fitness distributions and expanded structural complexity of individuals. From a dynamic point of view, an analysis of the causality of the crossover operator suggests that HGP discovers and exploits useful structures in a bottom-up, hierarchical manner. Diversity and causality are complementary, affecting exploration and exploitation in genetic search. Unlike other machine learning techniques that need extra machinery to control the tradeoff between them, HGP automatically trades off exploration and exploitation.

Connected through hyperedge HE-1 (induced by source paper "Evolution-based Discovery of Hierarchical Behaviors"):
  with members:
    - "An Experimental Analysis of Schema Creation, Propagation and Disruption in Genetic Programming"
    - "Evaluation and Selection of Biases in Machine Learning"

Connected through hyperedge HE-2 (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."
    - "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"
    - "A Comparison of Crossover and Mutation in Genetic Programming"
    - "An Analysis of the MAX Problem in Genetic Programming hold only in some cases, in"

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"
    - "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