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: "Independent Component Analysis by General Non-linear Hebbian-like Learning Rules"
  Abstract: A number of neural learning rules have been recently proposed for Independent Component Analysis (ICA). The rules are usually derived from information-theoretic criteria such as maximum entropy or minimum mutual information. In this paper, we show that in fact, ICA can be performed by very simple Hebbian or anti-Hebbian learning rules, which may have only weak relations to such information-theoretical quantities. Rather suprisingly, practically any non-linear function can be used in the learning rule, provided only that the sign of the Hebbian/anti-Hebbian term is chosen correctly. In addition to the Hebbian-like mechanism, the weight vector is here constrained to have unit norm, and the data is preprocessed by prewhitening, or sphering. These results imply that one can choose the non-linearity so as to optimize desired statistical or numerical criteria.

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 "A Fast Fixed-Point Algorithm for Independent Component Analysis"):
  with members:
    - "An information-maximisation approach to blind separation and blind deconvolution"
    - "A New Learning Algorithm for Blind Signal Separation"
    - "A FAMILY OF FIXED-POINT ALGORITHMS FOR INDEPENDENT COMPONENT ANALYSIS"
    - "Simple Neuron Models for Independent Component Analysis"

Connected through hyperedge HE-3 (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"
    - "A Fast Fixed-Point Algorithm for Independent Component Analysis"
    - "One-unit Learning Rules for Independent Component Analysis"

Connected through hyperedge HE-4 (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"
    - "A Fast Fixed-Point Algorithm for Independent Component Analysis"
    - "One-unit Learning Rules for Independent Component Analysis"

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"

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