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 "Better Trained Ants"):
  Abstract: The problem of programming an artificial ant to follow the Santa Fe trail has been repeatedly used as a benchmark problem. Recently we have shown performance of several techniques is not much better than the best performance obtainable using uniform random search. We suggested that this could be because the program fitness landscape is difficult for hill climbers and the problem is also difficult for Genetic Algorithms as it contains multiple levels of deception. Here we redefine the problem so the ant is obliged to traverse the trail in approximately the correct order. A simple genetic programming system, with no size or depth restriction, is show to perform approximately three times better with the improved training function.

Includes members:
  Member "Why Ants are Hard genetic programming, simulated annealing and hill climbing performance is shown not":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Genetic Programming with One-Point Crossover and Point Mutation")
  Member "The Evolution of Size in Variable Length Representations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Why Ants are Hard genetic programming, simulated annealing and hill climbing performance is shown not")

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