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: "a data dependent multiscale model for hyperspectral unmixing with spectral variability"
  Abstract: Spectral variability in hyperspectral images can result from factors including environmental, illumination, atmospheric and temporal changes. Its occurrence may lead to the propagation of significant estimation errors in the unmixing process. To address this issue, extended linear mixing models have been proposed which lead to large scale nonsmooth ill-posed inverse problems. Furthermore, the regularization strategies used to obtain meaningful results have introduced interdependencies among abundance solutions that further increase the complexity of the resulting optimization problem. In this paper we present a novel data dependent multiscale model for hyperspectral unmixing accounting for spectral variability. The new method incorporates spatial contextual information to the abundances in the Extended Linear Mixing Model by using a multiscale transform based on superpixels. The proposed method results in a fast algorithm that solves the abundance problem only once in each scale during each iteration. Simulation results using synthetic and real images compare the performances, both in accuracy and execution time, of the proposed algorithm and other state-of-the-art solutions.

Connected through hyperedge HE-1 (induced by source paper "deep generative endmember modeling an application to unsupervised spectral unmixing"):
  with members:
    - "improved hyperspectral unmixing with endmember variability parametrized using an interpolated scaling tensor"
    - "a low rank tensor regularization strategy for hyperspectral unmixing"
    - "an unsupervised approach to solving inverse problems using generative adversarial networks"
    - "super resolution for hyperspectral and multispectral image fusion accounting for seasonal spectral variability"
    - "low rank tensor modeling for hyperspectral unmixing accounting for spectral variability"
    - "improved deep spectral convolution network for hyperspectral unmixing with multinomial mixture kernel and endmember uncertainty"
    - "band selection for nonlinear unmixing of hyperspectral images as a maximal clique problem"
    - "generalized linear mixing model accounting for endmember variability"

Connected through hyperedge HE-2 (induced by source paper "improved hyperspectral unmixing with endmember variability parametrized using an interpolated scaling tensor"):
  with members:
    - "a low rank tensor regularization strategy for hyperspectral unmixing"
    - "super resolution for hyperspectral and multispectral image fusion accounting for seasonal spectral variability"
    - "band selection for nonlinear unmixing of hyperspectral images as a maximal clique problem"
    - "generalized linear mixing model accounting for endmember variability"
    - "nonparametric detection of nonlinearly mixed pixels and endmember estimation in hyperspectral images"

Connected through hyperedge HE-3 (induced by source paper "low rank tensor modeling for hyperspectral unmixing accounting for spectral variability"):
  with members:
    - "deep generative endmember modeling an application to unsupervised spectral unmixing"
    - "an augmented linear mixing model to address spectral variability for hyperspectral unmixing"
    - "improved hyperspectral unmixing with endmember variability parametrized using an interpolated scaling tensor"
    - "a low rank tensor regularization strategy for hyperspectral unmixing"
    - "super resolution for hyperspectral and multispectral image fusion accounting for seasonal spectral variability"
    - "band selection for nonlinear unmixing of hyperspectral images as a maximal clique problem"
    - "generalized linear mixing model accounting for endmember variability"
    - "nonparametric detection of nonlinearly mixed pixels and endmember estimation in hyperspectral images"

Connected through hyperedge HE-4 (induced by source paper "a low rank tensor regularization strategy for hyperspectral unmixing"):
  with members:
    - "band selection for nonlinear unmixing of hyperspectral images as a maximal clique problem"
    - "generalized linear mixing model accounting for endmember variability"
    - "tech report a fast multiscale spatial regularization for sparse hyperspectral unmixing"
    - "nonparametric detection of nonlinearly mixed pixels and endmember estimation in hyperspectral images"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"

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)