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 with One-Point Crossover and Point Mutation"
  Abstract: Technical Report: CSRP-97-13 April 1997 Abstract In recent theoretical and experimental work on schemata in genetic programming we have proposed a new simpler form of crossover in which the same crossover point is selected in both parent programs. We call this operator one-point crossover because of its similarity with the corresponding operator in genetic algorithms. One point crossover presents very interesting properties from the theory point of view. In this paper we describe this form of crossover as well as a new variant called strict one-point crossover highlighting their useful theoretical and practical features. We also present experimental evidence which shows that one-point crossover compares favourably with standard crossover.

Connected through hyperedge HE-1 (induced by source paper "An Experimental Analysis of Schema Creation, Propagation and Disruption in Genetic Programming"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "The Troubling Aspects of a Building Block Hypothesis for Genetic Programming"
    - "Evolution-based Discovery of Hierarchical Behaviors"
    - "The Schema Theorem and Price's Theorem"
    - "How Fitness Structure Affects Subsolution Acquisition in Genetic Programming"

Connected through hyperedge HE-2 (induced by source paper "Why Ants are Hard genetic programming, simulated annealing and hill climbing performance is shown not"):
  with members:
    - "The Troubling Aspects of a Building Block Hypothesis for Genetic Programming"
    - "Boolean Functions Fitness Spaces"
    - "Genetic Programming Bloat with Dynamic Fitness"
    - "Better Trained Ants"
    - "The Evolution of Size in Variable Length Representations"

Connected through hyperedge HE-3 (induced by source paper "Price's Theorem and the MAX Problem"):
  with members:
    - "The Troubling Aspects of a Building Block Hypothesis for Genetic Programming"
    - "The Schema Theorem and Price's Theorem"

Connected through hyperedge HE-4 (induced by source paper "An Analysis of the MAX Problem in Genetic Programming hold only in some cases, in"):
  with members:
    - "The Troubling Aspects of a Building Block Hypothesis for Genetic Programming"
    - "Modeling the Evolution of Motivation"
    - "The Schema Theorem and Price's Theorem"
    - "Evolutionary Programming and Evolution Strategies: Similarities and Differences"

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