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: "A New Learning Algorithm for Blind Signal Separation"
  Abstract: A new on-line learning algorithm which minimizes a statistical dependency among outputs is derived for blind separation of mixed signals. The dependency is measured by the average mutual information (MI) of the outputs. The source signals and the mixing matrix are unknown except for the number of the sources. The Gram-Charlier expansion instead of the Edgeworth expansion is used in evaluating the MI. The natural gradient approach is used to minimize the MI. A novel activation function is proposed for the on-line learning algorithm which has an equivariant property and is easily implemented on a neural network like model. The validity of the new learning algorithm is verified by computer simulations.

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

Connected through hyperedge HE-2 (induced by source paper "Equivariant adaptive source separation"):
  with members:
    - "SELF-ADAPTIVE NEURAL NETWORKS FOR BLIND SEPARATION OF SOURCES"
    - "NEURAL NETWORK APPROACH TO BLIND SEPARATION AND ENHANCEMENT OF IMAGES"
    - "A Blind Identification and Separation Technique via Multi-layer Neural Networks"
    - "LOCAL ADAPTIVE LEARNING ALGORITHMS FOR BLIND SEPARATION OF NATURAL IMAGES"
    - "Principal and Independent Components in Neural Networks Recent Developments"
    - "Independent Component Analysis of Simulated EEG Using a Three-Shell Spherical Head Model 1"
    - "Edges are the `Independent Components' of Natural Scenes."
    - "NIPS*97 Multiplicative Updating Rule for Blind Separation Derived from the Method of Scoring"

Connected through hyperedge HE-3 (induced by source paper "Analyzing Hyperspectral Data with Independent Component Analysis"):
  with members:
    - "An information-maximisation approach to blind separation and blind deconvolution"
    - "LEARNING LINEAR, SPARSE, FACTORIAL CODES"

Connected through hyperedge HE-4 (induced by source paper "NIPS*97 Multiplicative Updating Rule for Blind Separation Derived from the Method of Scoring"):
  with members:
    - "Equivariant adaptive source separation"

Connected through hyperedge HE-5 (induced by source paper "Maximum Likelihood and Covariant Algorithms for Independent Component Analysis somewhat more biologically plausible, involving no"):
  with members:
    - "An information-maximisation approach to blind separation and blind deconvolution"

Connected through hyperedge HE-6 (induced by source paper "LOCAL ADAPTIVE LEARNING ALGORITHMS FOR BLIND SEPARATION OF NATURAL IMAGES"):
  with members:
    - "An information-maximisation approach to blind separation and blind deconvolution"
    - "SELF-ADAPTIVE NEURAL NETWORKS FOR BLIND SEPARATION OF SOURCES"
    - "Equivariant adaptive source separation"

Connected through hyperedge HE-7 (induced by source paper "A Fast Fixed-Point Algorithm 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 FAMILY OF FIXED-POINT ALGORITHMS FOR INDEPENDENT COMPONENT ANALYSIS"
    - "Simple Neuron Models for Independent Component Analysis"

Connected through hyperedge HE-8 (induced by source paper "A Context-Sensitive Generalization of ICA"):
  with members:
    - "An information-maximisation approach to blind separation and blind deconvolution"
    - "BLIND SEPARATION OF DELAYED SOURCES BASED ON INFORMATION MAXIMIZATION"

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