human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "spectral unmixing of hyperspectral imagery using multilayer nmf"
  Abstract: Hyperspectral images contain mixed pixels due to low spatial resolution of hyperspectral sensors. Spectral unmixing problem refers to decomposing mixed pixels into a set of endmembers and abundance fractions. Due to nonnegativity constraint on abundance fractions, nonnegative matrix factorization (NMF) methods have been widely used for solving spectral unmixing problem. In this letter we proposed using multilayer NMF (MLNMF) for the purpose of hyperspectral unmixing. In this approach, spectral signature matrix can be modeled as a product of sparse matrices. In fact MLNMF decomposes the observation matrix iteratively in a number of layers. In each layer, we applied sparseness constraint on spectral signature matrix as well as on abundance fractions matrix. In this way signatures matrix can be sparsely decomposed despite the fact that it is not generally a sparse matrix. The proposed algorithm is applied on synthetic and real data sets. Synthetic data is generated based on endmembers from U.S. Geological Survey spectral library. AVIRIS Cuprite data set has been used as a real data set for evaluation of proposed method. Results of experiments are quantified based on SAD and AAD measures. Results in comparison with previously proposed methods show that the multilayer approach can unmix data more effectively.

Connected through hyperedge HE-1 (induced by source paper "deep self representative concept factorization network for representation learning"):
  with members:
    - "robust subspace clustering with compressed data"
    - "joint label prediction based semi supervised adaptive concept factorization for robust data representation"
    - "the trustworthy pal controlling the false discovery rate in boolean matrix factorization"
    - "robust subspace discovery by block diagonal adaptive locality constrained representation"
    - "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"
    - "flexible auto weighted local coordinate concept factorization a robust framework for unsupervised clustering"
    - "robust unsupervised flexible auto weighted local coordinate concept factorization for image clustering"
    - "did distributed incremental block coordinate descent for nonnegative matrix factorization"

Connected through hyperedge HE-2 (induced by source paper "clustered multitask nonnegative matrix factorization for spectral unmixing of hyperspectral data"):
  with members:
    - "sparsity constrained distributed unmixing of hyperspectral data"
    - "hyperspectral unmixing based on clustered multitask networks"
    - "structured sparse method for hyperspectral unmixing"
    - "diffusion least mean p power algorithms for distributed estimation in alpha stable noise environments"
    - "hyperspectral data unmixing using gnmf method and sparseness constraint"
    - "multitask diffusion adaptation over networks"

Connected through hyperedge HE-3 (induced by source paper "sparsity constrained distributed unmixing of hyperspectral data"):
  with members:
    - "distributed unmixing of hyperspectral data with sparsity constraint"
    - "sparsity constrained graph regularized nmf for spectral unmixing of hyperspectral data"
    - "multilayer structured nmf for spectral unmixing of hyperspectral images"
    - "diffusion least mean p power algorithms for distributed estimation in alpha stable noise environments"
    - "multitask diffusion adaptation over networks"
    - "diffusion adaptation over networks"

Connected through hyperedge HE-4 (induced by source paper "distributed unmixing of hyperspectral data with sparsity constraint"):
  with members:
    - "hyperspectral data unmixing using gnmf method and sparseness constraint"
    - "multitask diffusion adaptation over networks"
    - "diffusion adaptation over networks"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.CV(Computer Vision and Pattern Recognition)