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: "band selection for nonlinear unmixing of hyperspectral images as a maximal clique problem"
  Abstract: Kernel-based nonlinear mixing models have been applied to unmix spectral information of hyperspectral images when the type of mixing occurring in the scene is too complex or unknown. Such methods, however, usually require the inversion of matrices of sizes equal to the number of spectral bands. Reducing the computational load of these methods remains a challenge in large-scale applications. This paper proposes a centralized band selection (BS) method for supervised unmixing in the reproducing kernel Hilbert space. It is based upon the coherence criterion, which sets the largest value allowed for correlations between the basis kernel functions characterizing the selected bands in the unmixing model. We show that the proposed BS approach is equivalent to solving a maximum clique problem, i.e., searching for the biggest complete subgraph in a graph. Furthermore, we devise a strategy for selecting the coherence threshold and the Gaussian kernel bandwidth using coherence bounds for linearly independent bases. Simulation results illustrate the efficiency of the proposed method.

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"
    - "generalized linear mixing model accounting for endmember variability"

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"
    - "generalized linear mixing model accounting for endmember variability"
    - "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"
    - "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 "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"
    - "generalized linear mixing model accounting for endmember variability"
    - "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"
    - "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"

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"
    - "generalized linear mixing model accounting for endmember variability"
    - "nonparametric detection of nonlinearly mixed pixels and endmember estimation in hyperspectral images"

Connected through hyperedge HE-7 (induced by source paper "generalized linear mixing model accounting for endmember variability"):
  with members:
    - "nonparametric detection of nonlinearly mixed pixels and endmember estimation in hyperspectral images"

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)