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: "Growing Cell Structures A Self-organizing Network for Unsupervised and Supervised Learning"
  Abstract: We present a new self-organizing neural network model having two variants. The first variant performs unsupervised learning and can be used for data visualization, clustering, and vector quantization. The main advantage over existing approaches, e.g., the Kohonen feature map, is the ability of the model to automatically find a suitable network structure and size. This is achieved through a controlled growth process which also includes occasional removal of units. The second variant of the model is a supervised learning method which results from the combination of the abovementioned self-organizing network with the radial basis function (RBF) approach. In this model it is possible in contrast to earlier approaches toperform the positioning of the RBF units and the supervised training of the weights in parallel. Therefore, the current classification error can be used to determine where to insert new RBF units. This leads to small networks which generalize very well. Results on the two-spirals benchmark and a vowel classification problem are presented which are better than any results previously published. fl submitted for publication

Connected through hyperedge HE-1 (induced by source paper "LBG-U method for vector quantization an improvement over LBG inspired from neural networks"):
  with members:
    - "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"

Connected through hyperedge HE-2 (induced by source paper "GROWING RADIAL BASIS FUNCTION NETWORKS"):
  with members:
    - "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"
    - "Learning networks for face analysis and synthesis"
    - "Learning Controllers for Industrial Robots"
    - "GA-RBF: A Self-Optimising RBF Network"

Connected through hyperedge HE-3 (induced by source paper "Introduction to Radial Basis Function Networks"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Neural Networks and Statistical Models Proceedings of the Nineteenth Annual SAS Users Group International Conference,"
    - "Nonlinear gated experts for time series: discovering regimes and avoiding overfitting"

Connected through hyperedge HE-4 (induced by source paper "Evolving Artificial Neural Networks using the Baldwin Effect"):
  with members:
    - "Pruning Recurrent Neural Networks for Improved Generalization Performance"

Connected through hyperedge HE-5 (induced by source paper "Scatter-partitioning RBF network for function regression and image"):
  with members:
    (none)

Connected through hyperedge HE-6 (induced by source paper "Incremental Grid Growing: Encoding High-Dimensional Structure into a Two-Dimensional Feature Map"):
  with members:
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."

Connected through hyperedge HE-7 (induced by source paper "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"):
  with members:
    - "Topography And Ocular Dominance: A Model Exploring Positive Correlations"
    - "Constrained Optimization for Neural Map Formation: A Unifying Framework for Weight Growth and Normalization"
    - "Self-Organization and Functional Role of Lateral Connections and Multisize Receptive Fields in the Primary Visual Cortex"
    - "Generative Models for Discovering Sparse Distributed Representations"
    - "SCRIPT RECOGNITION WITH HIERARCHICAL FEATURE MAPS"
    - "SELF-ORGANIZING PROCESS BASED ON LATERAL INHIBITION AND SYNAPTIC RESOURCE REDISTRIBUTION"
    - "IEEE Learning the Semantic Similarity of Reusable Software Components"
    - "Self-Organization and Segmentation in a Laterally Connected Orientation Map of Spiking Neurons"

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