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: "Hybridized Crossover-Based Search Techniques for Program Discovery"
  Abstract: In this paper we address the problem of program discovery as defined by Genetic Programming [10]. We have two major results: First, by combining a hierarchical crossover operator with two traditional single point search algorithms: Simulated Annealing and Stochastic Iterated Hill Climbing, we have solved some problems with fewer fitness evaluations and a greater probability of a success than Genetic Programming. Second, we have managed to enhance Genetic Programming by hybridizing it with the simple scheme of hill climbing from a few individuals, at a fixed interval of generations. The new hill climbing component has two options for generating candidate solutions: mutation or crossover. When it uses crossover, mates are either randomly created, randomly drawn from the population at large, or drawn from a pool of fittest individuals.

Connected through hyperedge HE-1 (induced by source paper "Program Search with a Hierarchical Variable Length Representation: Genetic Programming, Simulated Annealing and Hill Climbing"):
  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"
    - "Investigating the Generality of Automatically Defined Functions"

Connected through hyperedge HE-2 (induced by source paper "An Adverse Interaction between the Crossover Operator and a Restriction on Tree Depth of Crossover"):
  with members:
    - "Genetic Programming and Redundancy"
    - "Context Preserving Crossover in Genetic Programming."
    - "Hierarchical Genetic Programming (HGP) extensions discover, modify, and exploit subroutines to accelerate the evolution of"

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