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: "STRUCTURAL LEARNING OF FUZZY RULES FROM NOISED EXAMPLES"
  Abstract: Inductive learning algorithms try to obtain the knowledge of a system from a set of examples. One of the most difficult problems in machine learning consists in getting the structure of this knowledge. We propose an algorithm able to manage with fuzzy information and able to learn the structure of the rules that represent the system. The algorithm gives a reasonable small set of fuzzy rules that represent the original set of examples.

Connected through hyperedge HE-1 (induced by source paper "Using Genetic Algorithms for Supervised Concept Learning"):
  with members:
    - "AN EMPIRICAL APPROACH TO SOLVING THE GENERAL UTILITY PROBLEM IN SPEEDUP LEARNING"
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Using Real-Valued Genetic Algorithms to Evolve Rule Sets for Classification"
    - "Using Neural Networks and Genetic Algorithms as Heuristics for NP-Complete Problems"
    - "Knowledge-Based Genetic Learning"
    - "Data Analyses Using Simulated Breeding and Inductive Learning Methods"
    - "Adaptive Strategy Selection for Concept Learning"
    - "A Survey of Evolution Strategies"

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