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: "hyperspectral and multispectral image fusion based on a sparse representation"
  Abstract: This paper presents a variational based approach to fusing hyperspectral and multispectral images. The fusion process is formulated as an inverse problem whose solution is the target image assumed to live in a much lower dimensional subspace. A sparse regularization term is carefully designed, relying on a decomposition of the scene on a set of dictionaries. The dictionary atoms and the corresponding supports of active coding coefficients are learned from the observed images. Then, conditionally on these dictionaries and supports, the fusion problem is solved via alternating optimization with respect to the target image (using the alternating direction method of multipliers) and the coding coefficients. Simulation results demonstrate the efficiency of the proposed algorithm when compared with the state-of-the-art fusion methods.

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 "deep convolutional neural network for multi modal image restoration and fusion"):
  with members:
    - "densefuse a fusion approach to infrared and visible images"
    - "feedback network for image super resolution"
    - "deformable kernel networks for joint image filtering"
    - "fast end to end trainable guided filter"
    - "multimodal image super resolution via joint sparse representations induced by coupled dictionaries"
    - "co occurrence filter"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "deepfuse a deep unsupervised approach for exposure fusion with extreme exposure image pairs"

Connected through hyperedge HE-3 (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"
    - "fast fusion of multi band images based on solving a sylvester equation"

Connected through hyperedge HE-4 (induced by source paper "unsupervised sparse dirichlet net for hyperspectral image super resolution"):
  with members:
    - "sparse signal recovery via generalized entropy functions minimization"
    - "endnet sparse autoencoder network for endmember extraction and hyperspectral unmixing"
    - "boosting the accuracy of multispectral image pansharpening by learning a deep residual network"
    - "recurrent neural networks to correct satellite image classification maps"
    - "aerial vehicle tracking by adaptive fusion of hyperspectral likelihood maps"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "densely connected convolutional networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"

Connected through hyperedge HE-5 (induced by source paper "image segmentation using subspace representation and sparse decomposition"):
  with members:
    - "masked signal decomposition using subspace representation and its applications"
    - "an experimental study of deep convolutional features for iris recognition"
    - "screen content image segmentation using robust regression and sparse decomposition"
    - "robust computation of linear models by convex relaxation"
    - "learning deconvolution network for semantic segmentation"
    - "fully convolutional networks for semantic segmentation"
    - "stable image reconstruction using total variation minimization"
    - "tilt transform invariant low rank textures"

Connected through hyperedge HE-6 (induced by source paper "scene adapted plug and play algorithm with guaranteed convergence applications to data fusion in imaging"):
  with members:
    - "the little engine that could regularization by denoising red"
    - "image restoration and reconstruction using variable splitting and class adapted image priors"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "algorithm induced prior for image restoration"
    - "adaptive image denoising by targeted databases"
    - "hyperspectral pansharpening a review"
    - "a convex formulation for hyperspectral image superresolution via subspace based regularization"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

Connected through hyperedge HE-7 (induced by source paper "multisource and multitemporal data fusion in remote sensing"):
  with members:
    - "effective sequential classifier training for svm based multitemporal remote sensing image classification"
    - "toward seamless multiview scene analysis from satellite to street level"
    - "comprehensive survey of deep learning in remote sensing theories tools and challenges for the community"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "a convex formulation for hyperspectral image superresolution via subspace based regularization"
    - "very deep convolutional networks for large scale image recognition"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-8 (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"
    - "fast fusion of multi band images based on solving a sylvester equation"

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)