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: "nonparametric detection of nonlinearly mixed pixels and endmember estimation in hyperspectral images"
  Abstract: Mixing phenomena in hyperspectral images depend on a variety of factors, such as the resolution of observation devices, the properties of materials, and how these materials interact with incident light in the scene. Different parametric and nonparametric models have been considered to address hyperspectral unmixing problems. The simplest one is the linear mixing model. Nevertheless, it has been recognized that the mixing phenomena can also be nonlinear. The corresponding nonlinear analysis techniques are necessarily more challenging and complex than those employed for linear unmixing. Within this context, it makes sense to detect the nonlinearly mixed pixels in an image prior to its analysis, and then employ the simplest possible unmixing technique to analyze each pixel. In this paper, we propose a technique for detecting nonlinearly mixed pixels. The detection approach is based on the comparison of the reconstruction errors using both a Gaussian process regression model and a linear regression model. The two errors are combined into a detection statistics for which a probability density function can be reasonably approximated. We also propose an iterative endmember extraction algorithm to be employed in combination with the detection algorithm. The proposed detect-then-unmix strategy, which consists of extracting endmembers, detecting nonlinearly mixed pixels and unmixing, is tested with synthetic and real images.

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

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

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"
    - "generalized linear mixing model accounting for endmember variability"
    - "tech report a fast multiscale spatial regularization for sparse hyperspectral unmixing"
    - "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"
    - "generalized linear mixing model accounting for endmember variability"

Connected through hyperedge HE-7 (induced by source paper "band selection for nonlinear unmixing of hyperspectral images as a maximal clique problem"):
  with members:
    - "band selection in rkhs for fast nonlinear unmixing of hyperspectral images"

Connected through hyperedge HE-8 (induced by source paper "generalized linear mixing model accounting for endmember variability"):
  with members:
    - "band selection for nonlinear unmixing of hyperspectral images as a maximal clique problem"

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)