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: "hyperspectral data unmixing using gnmf method and sparseness constraint"
  Abstract: Hyperspectral images contain mixed pixels due to low spatial resolution of hyperspectral sensors. Mixed pixels are pixels containing more than one distinct material called endmembers. The presence percentages of endmembers in mixed pixels are called abundance fractions. Spectral unmixing problem refers to decomposing these pixels into a set of endmembers and abundance fractions. Due to nonnegativity constraint on abundance fractions, nonnegative matrix factorization methods (NMF) have been widely used for solving spectral unmixing problem. In this paper we have used graph regularized (GNMF) method with sparseness constraint to unmix hyperspectral data. This method applied on simulated data using AVIRIS Indian Pines dataset and USGS library and results are quantified based on AAD and SAD measures. Results in comparison with other methods show that the proposed method can unmix data more effectively.

Connected through hyperedge HE-1 (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"
    - "spectral unmixing of hyperspectral imagery using multilayer nmf"
    - "structured sparse method for hyperspectral unmixing"
    - "diffusion least mean p power algorithms for distributed estimation in alpha stable noise environments"
    - "multitask diffusion adaptation over networks"

Connected through hyperedge HE-2 (induced by source paper "distributed unmixing of hyperspectral data with sparsity constraint"):
  with members:
    - "spectral unmixing of hyperspectral imagery using multilayer nmf"
    - "multitask diffusion adaptation over networks"
    - "diffusion adaptation over networks"

Connected through hyperedge HE-3 (induced by source paper "sparsity constrained graph regularized nmf for spectral unmixing of hyperspectral data"):
  with members:
    - "graph regularized nonnegative matrix factorization for hyperspectral data unmixing"

Connected through hyperedge HE-4 (induced by source paper "multilayer structured nmf for spectral unmixing of hyperspectral images"):
  with members:
    - "graph regularized nonnegative matrix factorization for hyperspectral data unmixing"

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)