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: "generalized linear mixing model accounting for endmember variability"
  Abstract: Endmember variability is an important factor for accurately unveiling vital information relating the pure materials and their distribution in hyperspectral images. Recently, the extended linear mixing model (ELMM) has been proposed as a modification of the linear mixing model (LMM) to consider endmember variability effects resulting mainly from illumination changes. In this paper, we further generalize the ELMM leading to a new model (GLMM) to account for more complex spectral distortions where different wavelength intervals can be affected unevenly. We also extend the existing methodology to jointly estimate the variability and the abundances for the GLMM. Simulations with real and synthetic data show that the unmixing process can benefit from the extra flexibility introduced by the GLMM.

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"
    - "a data dependent multiscale model for hyperspectral unmixing with spectral variability"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "deep generative models for library augmentation in multiple endmember spectral mixture analysis"):
  with members:
    - "deep generative endmember modeling an application to unsupervised spectral unmixing"
    - "low rank tensor modeling for hyperspectral unmixing accounting for spectral variability"
    - "band selection for nonlinear unmixing of hyperspectral images as a maximal clique problem"
    - "nonparametric detection of nonlinearly mixed pixels and endmember estimation in hyperspectral images"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (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"
    - "a data dependent multiscale model for hyperspectral unmixing with spectral variability"
    - "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"
    - "nonparametric detection of nonlinearly mixed pixels and endmember estimation in hyperspectral images"

Connected through hyperedge HE-4 (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"
    - "a data dependent multiscale model for hyperspectral unmixing with spectral variability"
    - "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"
    - "nonparametric detection of nonlinearly mixed pixels and endmember estimation in hyperspectral images"

Connected through hyperedge HE-5 (induced by source paper "a low rank tensor regularization strategy for hyperspectral unmixing"):
  with members:
    - "a data dependent multiscale model for hyperspectral unmixing with spectral variability"
    - "band selection for nonlinear unmixing of hyperspectral images as a maximal clique problem"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "a data dependent multiscale model for hyperspectral unmixing with 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"
    - "a low rank tensor regularization strategy for hyperspectral unmixing"
    - "band selection for nonlinear unmixing of hyperspectral images as a maximal clique problem"
    - "nonparametric detection of nonlinearly mixed pixels and endmember estimation in hyperspectral images"

Connected through hyperedge HE-7 (induced by source paper "super resolution for hyperspectral and multispectral image fusion accounting for seasonal spectral variability"):
  with members:
    - "a low rank tensor regularization strategy for hyperspectral unmixing"
    - "hyperspectral pansharpening a review"
    - "a convex formulation for hyperspectral image superresolution via subspace based regularization"
    - "fast fusion of multi band images based on solving a sylvester equation"
    - "hyperspectral and multispectral image fusion based on a sparse representation"

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)