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: "One-unit Learning Rules for Independent Component Analysis"
  Abstract: Neural one-unit learning rules for the problem of Independent Component Analysis (ICA) and blind source separation are introduced. In these new algorithms, every ICA neuron develops into a separator that tnds one of the independent components. The learning rules use very simple constrained Hebbian/anti-Hebbian learning in which decorrelating feedback may be added. To speed up the convergence of these stochastic gradient descent rules, a novel com putationally ecient txed-point algorithm is introduced.

Connected through hyperedge HE-1 (induced by source paper "Simple Neuron Models for Independent Component Analysis"):
  with members:
    - "An information-maximisation approach to blind separation and blind deconvolution"
    - "Equivariant adaptive source separation"
    - "Independent Component Analysis by General Non-linear Hebbian-like Learning Rules"
    - "A Fast Fixed-Point Algorithm for Independent Component Analysis"

Connected through hyperedge HE-2 (induced by source paper "A FAMILY OF FIXED-POINT ALGORITHMS FOR INDEPENDENT COMPONENT ANALYSIS"):
  with members:
    - "An information-maximisation approach to blind separation and blind deconvolution"
    - "Independent Component Analysis by General Non-linear Hebbian-like Learning Rules"
    - "A Fast Fixed-Point Algorithm for Independent Component Analysis"

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