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: "Scatter-partitioning RBF network for function regression and image"
  Abstract: segmentation: Preliminary results Abstract. Scatter-partitioning Radial Basis Function (RBF) networks increase their number of degrees of freedom with the complexity of an input-output mapping to be estimated on the basis of a supervised training data set. Due to its superior expressive power a scatter-partitioning Gaussian RBF (GRBF) model, termed Supervised Growing Neural Gas (SGNG), is selected from the literature. SGNG employs a one-stage error-driven learning strategy and is capable of generating and removing both hidden units and synaptic connections. A slightly modified SGNG version is tested as a function estimator when the training surface to be fitted is an image, i.e., a 2-D signal whose size is finite. The relationship between the generation, by the learning system, of disjointed maps of hidden units and the presence, in the image, of pictorially homogeneous subsets (segments) is investigated. Unfortunately, the examined SGNG version performs poorly both as function estimator and image segmenter. This may be due to an intrinsic inadequacy of the one-stage error-driven learning strategy to adjust structural parameters and output weights simultaneously but consistently. In the framework of RBF networks, further studies should investigate the combination of two-stage error-driven learning strategies with synapse generation and removal criteria. y Internal report of the paper entitled "Image segmentation with scatter-partitioning RBF networks: A feasibility study," to be presented at the conference Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation, part of SPIE's International Symposium on Optical Science, Engineering and Instrumentation, 19-24 July 1998, San Diego, CA.

Connected through hyperedge HE-1 (induced by source paper "Growing Cell Structures A Self-organizing Network for Unsupervised and Supervised Learning"):
  with members:
    - "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"
    - "Introduction to Radial Basis Function Networks"
    - "GROWING RADIAL BASIS FUNCTION NETWORKS"
    - "Evolving Artificial Neural Networks using the Baldwin Effect"
    - "LBG-U method for vector quantization an improvement over LBG inspired from neural networks"
    - "Incremental Grid Growing: Encoding High-Dimensional Structure into a Two-Dimensional Feature Map"

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