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: "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.

Connected through hyperedge HE-1 (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"
    - "Genetic Programming with One-Point Crossover and Point Mutation"
    - "Boolean Functions Fitness Spaces"
    - "Genetic Programming Bloat with Dynamic Fitness"
    - "The Evolution of Size in Variable Length Representations"

Connected through hyperedge HE-2 (induced by source paper "The Evolution of Size in Variable Length Representations"):
  with members:
    - "Why Ants are Hard genetic programming, simulated annealing and hill climbing performance is shown not"

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