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: "BLIND SEPARATION OF DELAYED SOURCES BASED ON INFORMATION MAXIMIZATION"
  Abstract: Recently, Bell and Sejnowski have presented an approach to blind source separation based on the information maximization principle. We extend this approach into more general cases where the sources may have been delayed with respect to each other. We present a network architecture capable of coping with such sources, and we derive the adaptation equations for the delays and the weights in the network by maximizing the information transferred through the network. Examples using wideband sources such as speech are presented to illustrate the algorithm.

Connected through hyperedge HE-1 (induced by source paper "A New Learning Algorithm for Blind Signal Separation"):
  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"
    - "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"
    - "LEARNING LINEAR, SPARSE, FACTORIAL CODES"
    - "Analyzing Hyperspectral Data with 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"
    - "A New Learning Algorithm for Blind Signal Separation"
    - "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."

Connected through hyperedge HE-3 (induced by source paper "A Context-Sensitive Generalization of ICA"):
  with members:
    - "An information-maximisation approach to blind separation and blind deconvolution"
    - "A New Learning Algorithm for Blind Signal Separation"

Connected through hyperedge HE-4 (induced by source paper "Blind separation of delayed and convolved sources."):
  with members:
    - "An information-maximisation approach to blind separation and blind deconvolution"
    - "A New Learning Algorithm for Blind Signal Separation"
    - "Equivariant adaptive source separation"
    - "BLIND SEPARATION OF REAL WORLD AUDIO SIGNALS USING OVERDETERMINED MIXTURES"

Connected through hyperedge HE-5 (induced by source paper "An information-maximisation approach to blind separation and blind deconvolution"):
  with members:
    - "Constrained Optimization for Neural Map Formation: A Unifying Framework for Weight Growth and Normalization"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "Working Paper IS-97-22 (Information Systems) A First Application of Independent Component Analysis to Extracting Structure"):
  with members:
    - "An information-maximisation approach to blind separation and blind deconvolution"
    - "Equivariant adaptive source separation"

Connected through hyperedge HE-7 (induced by source paper "BLIND SEPARATION OF REAL WORLD AUDIO SIGNALS USING OVERDETERMINED MIXTURES"):
  with members:
    - "A New Learning Algorithm for Blind Signal Separation"
    - "Blind separation of delayed and convolved sources."

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