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: "super resolution for hyperspectral and multispectral image fusion accounting for seasonal spectral variability"
  Abstract: Image fusion combines data from different heterogeneous sources to obtain more precise information about an underlying scene. Hyperspectral-multispectral (HS-MS) image fusion is currently attracting great interest in remote sensing since it allows the generation of high spatial resolution HS images, circumventing the main limitation of this imaging modality. Existing HS-MS fusion algorithms, however, neglect the spectral variability often existing between images acquired at different time instants. This time difference causes variations in spectral signatures of the underlying constituent materials due to different acquisition and seasonal conditions. This paper introduces a novel HS-MS image fusion strategy that combines an unmixing-based formulation with an explicit parametric model for typical spectral variability between the two images. Simulations with synthetic and real data show that the proposed strategy leads to a significant performance improvement under spectral variability and state-of-the-art performance otherwise.

Connected through hyperedge HE-1 (induced by source paper "mdlatlrr a novel decomposition method for infrared and visible image fusion"):
  with members:
    - "an accelerated correlation filter tracker"
    - "joint group feature selection and discriminative filter learning for robust visual object tracking"
    - "infrared and visible image fusion with resnet and zero phase component analysis"
    - "densefuse a fusion approach to infrared and visible images"
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "rgb t object tracking benchmark and baseline"
    - "infrared and visible image fusion using a deep learning framework"
    - "image fusion with cosparse analysis operator"

Connected through hyperedge HE-2 (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"
    - "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-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"
    - "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 "robust online video super resolution using an efficient alternating projections scheme"):
  with members:
    - "a new adaptive video super resolution algorithm with improved robustness to innovations"
    - "detail revealing deep video super resolution"
    - "super resolution reconstruction of electrical impedance tomography images"
    - "recent progress in image deblurring"

Connected through hyperedge HE-5 (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"
    - "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"

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)