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 Fast Fixed-Point Algorithm for Independent Component Analysis"
  Abstract: This paper will appear in Neural Computation, 9:1483-1492, 1997. Abstract We introduce a novel fast algorithm for Independent Component Analysis, which can be used for blind source separation and feature extraction. It is shown how a neural network learning rule can be transformed into a txed-point iteration, which provides an algorithm that is very simple, does not depend on any user-detned parameters, and is fast to converge to the most accurate solution allowed by the data. The algorithm tnds, one at a time, all non-Gaussian independent components, regardless of their probability distributions. The computations can be performed either in batch mode or in a semi-adaptive manner. The convergence of the algorithm is rigorously proven, and the convergence speed is shown to be cubic. Some comparisons to gradient based algorithms are made, showing that the new algorithm is usually 10 to 100 times faster, sometimes giving the solution in just a few iterations.

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 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-2 (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-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"
    - "Independent Component Analysis by General Non-linear Hebbian-like Learning Rules"
    - "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"
    - "Independent Component Analysis by General Non-linear Hebbian-like Learning Rules"
    - "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