human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Program Search with a Hierarchical Variable Length Representation: Genetic Programming, Simulated Annealing and Hill Climbing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.")
  Member "An Adverse Interaction between the Crossover Operator and a Restriction on Tree Depth of Crossover":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Hierarchical Genetic Programming (HGP) extensions discover, modify, and exploit subroutines to accelerate the evolution of")
  Member "The MAX Problem for Genetic Programming Highlighting an Adverse Interaction between the Crossover Operator and":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Hierarchical Genetic Programming (HGP) extensions discover, modify, and exploit subroutines to accelerate the evolution of")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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