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: "Balancing Accuracy and Parsimony in Genetic Programming 1"
  Abstract: Genetic programming is distinguished from other evolutionary algorithms in that it uses tree representations of variable size instead of linear strings of fixed length. The flexible representation scheme is very important because it allows the underlying structure of the data to be discovered automatically. One primary difficulty, however, is that the solutions may grow too big without any improvement of their generalization ability. In this paper we investigate the fundamental relationship between the performance and complexity of the evolved structures. The essence of the parsimony problem is demonstrated empirically by analyzing error landscapes of programs evolved for neural network synthesis. We consider genetic programming as a statistical inference problem and apply the Bayesian model-comparison framework to introduce a class of fitness functions with error and complexity terms. An adaptive learning method is then presented that automatically balances the model-complexity factor to evolve parsimonious programs without losing the diversity of the population needed for achieving the desired training accuracy. The effectiveness of this approach is empirically shown on the induction of sigma-pi neural networks for solving a real-world medical diagnosis problem as well as benchmark tasks.

Connected through hyperedge HE-1 (induced by source paper "Evolving Compact Solutions in Genetic Programming: A Case Study"):
  with members:
    - "A Comparison of Selection Schemes used in Genetic Algorithms"
    - "Genetic Programming and Redundancy"
    - "Causality in Genetic Programming"
    - "Complexity Compression and Evolution"

Connected through hyperedge HE-2 (induced by source paper "Generality versus Size in Genetic Programming"):
  with members:
    - "Generalization in Reinforcement Learning: Safely Approximating the Value Function"
    - "Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding"
    - "Evolving Visual Routines"
    - "Evolving a Generalised Behaviour: Artificial Ant Problem Revisited"

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