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 Role of Development in Genetic Algorithms"
  Abstract: Technical Report Number CS94-394 Computer Science and Engineering, U.C.S.D. Abstract The developmental mechanisms transforming genotypic to phenotypic forms are typically omitted in formulations of genetic algorithms (GAs) in which these two representational spaces are identical. We argue that a careful analysis of developmental mechanisms is useful when understanding the success of several standard GA techniques, and can clarify the relationships between more recently proposed enhancements. We provide a framework which distinguishes between two developmental mechanisms | learning and maturation | while also showing several common effects on GA search. This framework is used to analyze how maturation and local search can change the dynamics of the GA. We observe that in some contexts, maturation and local search can be incorporated into the fitness evaluation, but illustrate reasons for considering them seperately. Further, we identify contexts in which maturation and local search can be distinguished from the fitness evaluation.

Connected through hyperedge HE-1 (induced by source paper "A Comparison between Cellular Encoding and Direct Encoding for Genetic Neural Networks"):
  with members:
    - "An Evolutionary Method to Find Good Building-Blocks for Architectures of Artificial Neural Networks"
    - "Discovery of Symbolic, Neuro-Symbolic and Neural Networks with Parallel Distributed Genetic Programming"
    - "Cellular Encoding Applied to Neurocontrol"
    - "AUTOMATED TOPOLOGY AND SIZING OF ANALOG CIRCUITS AUTOMATED DESIGN OF BOTH THE TOPOLOGY AND SIZING"
    - "Evolving Deterministic Finite Automata Using Cellular Encoding programming and cellular encoding. Programs are evolved that"

Connected through hyperedge HE-2 (induced by source paper "Evolution in Time and Space The Parallel Genetic Algorithm"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Island Model Genetic Algorithms and Linearly Separable Problems"
    - "A Study of Genetic Algorithms to Find Approximate Solutions to Hard 3CNF Problems"
    - "Recombination Operator, its Correlation to the Fitness Landscape and Search Performance"
    - "Genetic Algorithm Programming Environments"
    - "A Survey of Parallel Genetic Algorithms"
    - "Staged Hybrid Genetic Search for Seismic Data Imaging"
    - "The Schema Theorem and Price's Theorem"

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