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: "Constrained Optimization for Neural Map Formation: A Unifying Framework for Weight Growth and Normalization"
  Abstract: Computational models of neural map formation can be considered on at least three different levels of abstraction: detailed models including neural activity dynamics, weight dynamics that abstract from the neural activity dynamics by an adiabatic approximation, and constrained optimization from which equations governing weight dynamics can be derived. Constrained optimization uses an objective function, from which a weight growth rule can be derived as a gradient flow, and some constraints, from which normalization rules are derived. In this paper we present an example of how an optimization problem can be derived from detailed non-linear neural dynamics. A systematic investigation reveals how different weight dynamics introduced previously can be derived from two types of objective function terms and two types of constraints. This includes dynamic link matching as a special case of neural map formation. We focus in particular on the role of coordinate transformations to derive different weight dynamics from the same optimization problem. Several examples illustrate how the constrained optimization framework can help in understanding, generating, and comparing different models of neural map formation. The techniques used in this analysis may also be useful in investigating other types of neural dynamics.

Connected through hyperedge HE-1 (induced by source paper "Theory of Correlations in Stochastic Neural Networks"):
  with members:
    - "Boltzmann Machine learning using mean field theory and linear response correction"

Connected through hyperedge HE-2 (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"
    - "Analyzing Phase Transitions in High-Dimensional Self-Organizing Maps"
    - "Self-Organization and Functional Role of Lateral Connections and Multisize Receptive Fields in the Primary Visual Cortex"

Connected through hyperedge HE-3 (induced by source paper "Analysis of Linsker's application of Hebbian rules to Linear Networks"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "SEMILINEAR PREDICTABILITY MINIMIZATION PRODUCES WELL-KNOWN FEATURE DETECTORS Neural Computation, 1996 (accepted)"
    - "The Role of Constraints in Hebbian Learning"

Connected through hyperedge HE-4 (induced by source paper "An information-maximisation approach to blind separation and blind deconvolution"):
  with members:
    - "Competitive Anti-Hebbian Learning of Invariants"
    - "SELF-ADAPTIVE NEURAL NETWORKS FOR BLIND SEPARATION OF SOURCES"
    - "A New Learning Algorithm for Blind Signal Separation"
    - "LOCAL ADAPTIVE LEARNING ALGORITHMS FOR BLIND SEPARATION OF NATURAL IMAGES"
    - "LEARNING FACTORIAL CODES BY PREDICTABILITY MINIMIZATION (Neural Computation, 4(6):863-879, 1992)"
    - "Analyzing Hyperspectral Data with Independent Component Analysis"
    - "Independent Component Analysis of Electroencephalographic Data"
    - "Principal and Independent Components in Neural Networks Recent Developments"

Connected through hyperedge HE-5 (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"
    - "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"
    - "Soft Computing: the Convergence of Emerging Reasoning Technologies"

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