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: "SUPERVISED COMPETITIVE LEARNING FOR FINDING POSITIONS OF RADIAL BASIS FUNCTIONS"
  Abstract: This paper introduces the magnetic neural gas (MNG) algorithm, which extends unsupervised competitive learning with class information to improve the positioning of radial basis functions. The basic idea of MNG is to discover heterogeneous clusters (i.e., clusters with data from different classes) and to migrate additional neurons towards them. The discovery is effected by a heterogeneity coefficient associated with each neuron and the migration is guided by introducing a kind of magnetic effect. The performance of MNG is tested on a number of data sets, including the thyroid data set. Results demonstrate promise.

Connected through hyperedge HE-1 (induced by source paper "Evolving Fuzzy Prototypes for Efficient Data Clustering"):
  with members:
    - "GA-RBF: A Self-Optimising RBF Network"

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: Neural_Networks