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: "Working Paper IS-97-22 (Information Systems) A First Application of Independent Component Analysis to Extracting Structure"
  Abstract: This paper discusses the application of a modern signal processing technique known as independent component analysis (ICA) or blind source separation to multivariate financial time series such as a portfolio of stocks. The key idea of ICA is to linearly map the observed multivariate time series into a new space of statistically independent components (ICs). This can be viewed as a factorization of the portfolio since joint probabilities become simple products in the coordinate system of the ICs. We apply ICA to three years of daily returns of the 28 largest Japanese stocks and compare the results with those obtained using principal component analysis. The results indicate that the estimated ICs fall into two categories, (i) infrequent but large shocks (responsible for the major changes in the stock prices), and (ii) frequent smaller fluctuations (contributing little to the overall level of the stocks). We show that the overall stock price can be reconstructed surprisingly well by using a small number of thresholded weighted ICs. In contrast, when using shocks derived from principal components instead of independent components, the reconstructed price is less similar to the original one. Independent component analysis is a potentially powerful method of analyzing and understanding driving mechanisms in financial markets. There are further promising applications to risk management since ICA focuses on higher order statistics.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "BLIND SEPARATION OF DELAYED SOURCES BASED ON INFORMATION MAXIMIZATION"):
  with members:
    - "An information-maximisation approach to blind separation and blind deconvolution"
    - "A New Learning Algorithm for Blind Signal Separation"
    - "Equivariant adaptive source separation"
    - "A Context-Sensitive Generalization of ICA"
    - "BLIND SEPARATION OF REAL WORLD AUDIO SIGNALS USING OVERDETERMINED MIXTURES"
    - "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