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: "r fuse robust fast fusion of multiband images based on solving a sylvester equation"
  Abstract: This letter proposes a robust fast multiband image fusion method to merge a high-spatial low-spectral resolution image and a low-spatial high-spectral resolution image. Following the method recently developed by Wei   et al. , the generalized Sylvester matrix equation associated with the multiband image fusion problem is solved in a more robust and efficient way by exploiting the Woodbury formula, avoiding any permutation operation in the frequency domain as well as the blurring kernel invertibility assumption required in their method. Thanks to this improvement, the proposed algorithm requires fewer computational operations and is also more robust with respect to the blurring kernel compared with the one developed by Wei  et al.  The proposed new algorithm is tested with different priors considered by Wei  et al.  Our conclusion is that the proposed fusion algorithm is more robust than the one by Wei  et al.  with a reduced computational cost.

Connected through hyperedge HE-1 (induced by source paper "hyperspectral and multispectral image fusion under spectrally varying spatial blurs application to high dimensional infrared astronomical imaging"):
  with members:
    - "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"

Connected through hyperedge HE-2 (induced by source paper "an inner loop free solution to inverse problems using deep neural networks"):
  with members:
    - "one network to solve them all solving linear inverse problems using deep projection models"
    - "a deep cascade of convolutional neural networks for mr image reconstruction"
    - "learning representations for automatic colorization"
    - "amortised map inference for image super resolution"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "plug play generative networks conditional iterative generation of images in latent space"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "super resolution with deep convolutional sufficient statistics"

Connected through hyperedge HE-3 (induced by source paper "fusing multiple multiband images"):
  with members:
    - "hyperspectral image recovery via hybrid regularization"
    - "multiband image fusion based on spectral unmixing"
    - "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"
    - "fast fusion of multi band images based on solving a sylvester equation"
    - "hyperspectral and multispectral image fusion based on a sparse representation"
    - "bayesian fusion of multi band images"

Connected through hyperedge HE-4 (induced by source paper "multiband image fusion based on spectral unmixing"):
  with members:
    - "hyperspectral pansharpening a review"
    - "a convex formulation for hyperspectral image superresolution via subspace based regularization"
    - "fast spectral unmixing based on dykstra s alternating projection"
    - "fast fusion of multi band images based on solving a sylvester equation"
    - "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)