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: "Evolution of the Topology and the Weights of Neural Networks using Genetic Programming with a"
  Abstract: Genetic programming is a methodology for program development, consisting of a special form of genetic algorithm capable of handling parse trees representing programs, that has been successfully applied to a variety of problems. In this paper a new approach to the construction of neural networks based on genetic programming is presented. A linear chromosome is combined to a graph representation of the network and new operators are introduced, which allow the evolution of the architecture and the weights simultaneously without the need of local weight optimization. This paper describes the approach, the operators and reports results of the application of the model to several binary classification problems.

Connected through hyperedge HE-1 (induced by source paper "Soft Computing: the Convergence of Emerging Reasoning Technologies"):
  with members:
    - "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"
    - "Dynamic Control of Genetic Algorithms using Fuzzy Logic Techniques"
    - "Genetic Algorithms for Automated Tuning of Fuzzy Controllers: A Transportation Application"

Connected through hyperedge HE-2 (induced by source paper "Genetic Encoding Strategies for Neural Networks"):
  with members:
    - "Representation and Evolution of Neural Networks"

Connected through hyperedge HE-3 (induced by source paper "A Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks"):
  with members:
    - "What do Constructive Learners Really Learn?"
    - "Unsupervised Constructive Learning"
    - "Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules"

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