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: "Analyzing Phase Transitions in High-Dimensional Self-Organizing Maps"
  Abstract: The Self-Organizing Map (SOM), a widely used algorithm for the unsupervised learning of neural maps, can be formulated in a low-dimensional "feature map" variant which requires prespecified parameters ("features") for the description of receptive fields, or in a more general high-dimensional variant which allows to self-organize the structure of individual receptive fields as well as their arrangement in a map. We present here a new analytical method to derive conditions for the emergence of structure in SOMs which is particularly suited for the as yet inaccessible high-dimensional SOM variant. Our approach is based on an evaluation of a map distortion function. It involves only an ansatz for the way stimuli are distributed among map neurons; the receptive fields of the map need not be known explicitely. Using this method we first calculate regions of stability for four possible states of SOMs projecting from a rectangular input space to a ring of neurons. We then analyze the transition from non-oriented to oriented receptive fields in a SOM-based model for the development of orientation maps. In both cases, the analytical results are well corroborated by the results of computer simulations. submitted to Biological Cybernetics, December 14, 1995 revised version, July 14, 1996

Connected through hyperedge HE-1 (induced by source paper "Topography And Ocular Dominance: A Model Exploring Positive Correlations"):
  with members:
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"
    - "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"

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