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: "The Evolution of Size in Variable Length Representations"
  Abstract: In many cases programs length's increase (known as bloat, fluff and increasing structural complexity) during artificial evolution. We show bloat is not specific to genetic programming and suggest it is inherent in search techniques with discrete variable length representations using simple static evaluation functions. We investigate the bloating characteristics of three non-population and one population based search techniques using a novel mutation operator. An artificial ant following the Santa Fe trail problem is solved by simulated annealing, hill climbing, strict hill climbing and population based search using two variants of the the new subtree based mutation operator. As predicted bloat is observed when using unbiased mutation and is absent in simulated annealing and both hill climbers when using the length neutral mutation however bloat occurs with both mutations when using a population. We conclude that there are two causes of bloat 1) search operators with no length bias tend to sample bigger trees and 2) competition within populations favours longer programs as they can usually reproduce more accurately.

Connected through hyperedge HE-1 (induced by source paper "Better Trained Ants"):
  with members:
    - "Why Ants are Hard genetic programming, simulated annealing and hill climbing performance is shown not"

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"
    - "Genetic Programming with One-Point Crossover and Point Mutation"
    - "Boolean Functions Fitness Spaces"
    - "Genetic Programming Bloat with Dynamic Fitness"
    - "Better Trained Ants"

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