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: "Genetic Encoding Strategies for Neural Networks"
  Abstract: The application of genetic algorithms to neural network optimization (GANN) has produced an active field of research. This paper proposes a classification of the encoding strategies and it also gives a critical analysis of the current state of development. The idea of evolving artificial neural networks (NN) by genetic algorithms (GA) is based on a powerful metaphor: the evolution of the human brain. This mechanism has developed the highest form of intelligence known from scratch. The metaphor has inspired a great deal of research activities that can be traced to the late 1980s (for instance [15]). An increasing amount of research reports, journal papers and theses have been published on the topic, generating a conti-nously growing field. Researchers have devoloped a variety of different techniques to encode neural networks for the GA, with increasing complexity. This young field is driven mostly by small, independet research groups that scarcely cooperate with each other. This paper will attempt to analyse and to structure the already performed work, and to point out the shortcomings of the approaches.

Connected through hyperedge HE-1 (induced by source paper "Evolution of the Topology and the Weights of Neural Networks using Genetic Programming with a"):
  with members:
    - "Soft Computing: the Convergence of Emerging Reasoning Technologies"
    - "A Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks"

Connected through hyperedge HE-2 (induced by source paper "Representation and Evolution of Neural Networks"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Dynamic Parameter Encoding for Genetic Algorithms"
    - "LEARNING BY ERROR-DRIVEN DECOMPOSITION"
    - "Feature selection through Functional Links with Evolutionary Computation for Neural Networks"

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