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: "An information-maximisation approach to blind separation and blind deconvolution"
  Abstract: We derive a new self-organising learning algorithm which maximises the information transferred in a network of non-linear units. The algorithm does not assume any knowledge of the input distributions, and is defined here for the zero-noise limit. Under these conditions, information maximisation has extra properties not found in the linear case (Linsker 1989). The non-linearities in the transfer function are able to pick up higher-order moments of the input distributions and perform something akin to true redundancy reduction between units in the output representation. This enables the network to separate statistically independent components in the inputs: a higher-order generalisation of Principal Components Analysis. We apply the network to the source separation (or cocktail party) problem, successfully separating unknown mixtures of up to ten speakers. We also show that a variant on the network architecture is able to perform blind deconvolution (cancellation of unknown echoes and reverberation in a speech signal). Finally, we derive dependencies of information transfer on time delays. We suggest that information max-imisation provides a unifying framework for problems in `blind' signal processing. fl Please send comments to tony@salk.edu. This paper will appear as Neural Computation, 7, 6, 1004-1034 (1995). The reference for this version is: Technical Report no. INC-9501, February 1995, Institute for Neural Computation, UCSD, San Diego, CA 92093-0523.

Connected through hyperedge HE-1 (induced by source paper "Independent Component Analysis by General Non-linear Hebbian-like Learning Rules"):
  with members:
    - "A New Learning Algorithm for Blind Signal Separation"
    - "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 "A New Learning Algorithm for Blind Signal Separation"):
  with members:
    - "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"
    - "Independent Component Analysis of Simulated EEG Using a Three-Shell Spherical Head Model 1"

Connected through hyperedge HE-3 (induced by source paper "Independent Component Analysis of Electroencephalographic Data"):
  with members:
    - "JUNG ET AL.: ESTIMATING ALERTNESS FORM THE EEG POWER SPECTRUM 1 Estimating Alertness from the"

Connected through hyperedge HE-4 (induced by source paper "Analyzing Hyperspectral Data with Independent Component Analysis"):
  with members:
    - "A New Learning Algorithm for Blind Signal Separation"
    - "LEARNING LINEAR, SPARSE, FACTORIAL CODES"

Connected through hyperedge HE-5 (induced by source paper "Principal and Independent Components in Neural Networks Recent Developments"):
  with members:
    - "Equivariant adaptive source separation"

Connected through hyperedge HE-6 (induced by source paper "Maximum Likelihood and Covariant Algorithms for Independent Component Analysis somewhat more biologically plausible, involving no"):
  with members:
    - "A New Learning Algorithm for Blind Signal Separation"

Connected through hyperedge HE-7 (induced by source paper "Learning Viewpoint Invariant Representations of Faces in an Attractor Network"):
  with members:
    - "A self-organizing multiple-view representation of 3D objects"
    - "A Model of Invariant Object Recognition in the Visual System"

Connected through hyperedge HE-8 (induced by source paper "LOCAL ADAPTIVE LEARNING ALGORITHMS FOR BLIND SEPARATION OF NATURAL IMAGES"):
  with members:
    - "SELF-ADAPTIVE NEURAL NETWORKS FOR BLIND SEPARATION OF SOURCES"
    - "A New Learning Algorithm for Blind Signal Separation"
    - "Equivariant adaptive source separation"

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