human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Topography And Ocular Dominance: A Model Exploring Positive Correlations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Analyzing Phase Transitions in High-Dimensional Self-Organizing Maps")
  Member "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Soft Computing: the Convergence of Emerging Reasoning Technologies")
  Member "An information-maximisation approach to blind separation and blind deconvolution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Independent Component Analysis by General Non-linear Hebbian-like Learning Rules")
  Member "Theory of Correlations in Stochastic Neural Networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Boltzmann Machine learning using mean field theory and linear response correction")
  Member "Analysis of Linsker's application of Hebbian rules to Linear Networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "The Role of Constraints in Hebbian Learning")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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