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: "Analyzing Hyperspectral Data with Independent Component Analysis"
  Abstract: Hyperspectral image sensors provide images with a large number of contiguous spectral channels per pixel and enable information about different materials within a pixel to be obtained. The problem of spectrally unmixing materials may be viewed as a specific case of the blind source separation problem where data consists of mixed signals (in this case minerals) and the goal is to determine the contribution of each mineral to the mix without prior knowledge of the minerals in the mix. The technique of Independent Component Analysis (ICA) assumes that the spectral components are close to statistically independent and provides an unsupervised method for blind source separation. We introduce contextual ICA in the context of hyperspectral data analysis and apply the method to mineral data from synthetically mixed minerals and real image signatures.

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"
    - "Independent Component Analysis of Simulated EEG Using a Three-Shell Spherical Head Model 1"

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)"
    - "Independent Component Analysis of Electroencephalographic Data"
    - "Principal and Independent Components in Neural Networks Recent Developments"

Connected through hyperedge HE-3 (induced by source paper "LEARNING LINEAR, SPARSE, FACTORIAL CODES"):
  with members:
    - "A New Learning Algorithm for Blind Signal 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