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: "fast fusion of multi band images based on solving a sylvester equation"
  Abstract: This paper proposes a fast multi-band image fusion algorithm, which combines a high-spatial low-spectral resolution image and a low-spatial high-spectral resolution image. The well admitted forward model is explored to form the likelihoods of the observations. Maximizing the likelihoods leads to solving a Sylvester equation. By exploiting the properties of the circulant and downsampling matrices associated with the fusion problem, a closed-form solution for the corresponding Sylvester equation is obtained explicitly, getting rid of any iterative update step. Coupled with the alternating direction method of multipliers and the block coordinate descent method, the proposed algorithm can be easily generalized to incorporate prior information for the fusion problem, allowing a Bayesian estimator. Simulation results show that the proposed algorithm achieves the same performance as the existing algorithms with the advantage of significantly decreasing the computational complexity of these algorithms.

Connected through hyperedge HE-1 (induced by source paper "unsupervised and unregistered hyperspectral image super resolution with mutual dirichlet net"):
  with members:
    - "unsupervised sparse dirichlet net for hyperspectral image super resolution"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "fast and accurate single image super resolution via information distillation network"
    - "deep back projection networks for super resolution"
    - "image super resolution via dual state recurrent networks"
    - "mine mutual information neural estimation"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "fsrnet end to end learning face super resolution with facial priors"

Connected through hyperedge HE-2 (induced by source paper "multispectral and hyperspectral image fusion by ms hs fusion net"):
  with members:
    - "boosting the accuracy of multispectral image pansharpening by learning a deep residual network"
    - "admm net a deep learning approach for compressive sensing mri"
    - "learning fully convolutional networks for iterative non blind deconvolution"
    - "hyperspectral pansharpening a review"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "image super resolution using deep convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "a symmetric encoder decoder with residual block for infrared and visible image fusion"):
  with members:
    - "densefuse a fusion approach to infrared and visible images"
    - "deepfuse a deep unsupervised approach for exposure fusion with extreme exposure image pairs"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "hyperspectral and multispectral image fusion under spectrally varying spatial blurs application to high dimensional infrared astronomical imaging"):
  with members:
    - "r fuse robust fast fusion of multiband images based on solving a sylvester equation"
    - "hyperspectral pansharpening a review"
    - "a convex formulation for hyperspectral image superresolution via subspace based regularization"
    - "hyperspectral and multispectral image fusion based on a sparse representation"

Connected through hyperedge HE-5 (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"
    - "generalized linear mixing model accounting for endmember variability"
    - "hyperspectral pansharpening a review"
    - "a convex formulation for hyperspectral image superresolution via subspace based regularization"
    - "hyperspectral and multispectral image fusion based on a sparse representation"

Connected through hyperedge HE-6 (induced by source paper "a gaussian mixture model representation of endmember variability in hyperspectral unmixing"):
  with members:
    - "a spatial compositional model for linear unmixing and endmember uncertainty estimation"

Connected through hyperedge HE-7 (induced by source paper "learned spectral super resolution"):
  with members:
    - "learning representations for automatic colorization"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "densely connected convolutional networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "colorful image colorization"
    - "deep residual learning for image recognition"
    - "a convex formulation for hyperspectral image superresolution via subspace based regularization"

Connected through hyperedge HE-8 (induced by source paper "fusing multiple multiband images"):
  with members:
    - "hyperspectral image recovery via hybrid regularization"
    - "multiband image fusion based on spectral unmixing"
    - "r fuse robust fast fusion of multiband images based on solving a sylvester equation"
    - "fast and robust pushbroom hyperspectral imaging via dmd based scanning"
    - "hyperspectral pansharpening a review"
    - "a convex formulation for hyperspectral image superresolution via subspace based regularization"
    - "hyperspectral and multispectral image fusion based on a sparse representation"
    - "bayesian fusion of multi band 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)