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 Blind Identification and Separation Technique via Multi-layer Neural Networks"
  Abstract: This paper deals with the problem of blind identification and source separation which consists of estimation of the mixing matrix and/or the separation of a mixture of stochastically independent sources without a priori knowledge on the mixing matrix . The method we propose here estimates the mixture matrix by a recurrent Input-Output (IO) Identification using as inputs a nonlinear transformation of the estimated sources. Herein, the nonlinear transformation (distortion) consists in constraining the modulus of the inputs of the IO-Identification device to be a constant. In contrast to other existing approaches, the covariance of the additive noise do not need to be modeled and can be estimated as a regular parameter if needed. The proposed approach is implemented using multi-layer neural networks in order to improve performance of separation. New associated on-line un-supervised adaptive learning rules are also developed. The effectiveness of the proposed method is illustrated by some computer simulations.

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"
    - "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-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"
    - "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 "SELF-ADAPTIVE NEURAL NETWORKS FOR BLIND SEPARATION OF 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"
    - "NEURAL NETWORK APPROACH TO BLIND SEPARATION AND ENHANCEMENT OF IMAGES"
    - "LOCAL ADAPTIVE LEARNING ALGORITHMS FOR BLIND SEPARATION OF NATURAL IMAGES"

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