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: "a convex formulation for hyperspectral image superresolution via subspace based regularization"
  Abstract: Hyperspectral remote sensing images (HSIs) usually have high spectral resolution and low spatial resolution. Conversely, multispectral images (MSIs) usually have low spectral and high spatial resolutions. The problem of inferring images that combine the high spectral and high spatial resolutions of HSIs and MSIs, respectively, is a data fusion problem that has been the focus of recent active research due to the increasing availability of HSIs and MSIs retrieved from the same geographical area. We formulate this problem as the minimization of a convex objective function containing two quadratic data-fitting terms and an edge-preserving regularizer. The data-fitting terms account for blur, different resolutions, and additive noise. The regularizer, a form of vector total variation, promotes piecewise-smooth solutions with discontinuities aligned across the hyperspectral bands. The downsampling operator accounting for the different spatial resolutions, the nonquadratic and nonsmooth nature of the regularizer, and the very large size of the HSI to be estimated lead to a hard optimization problem. We deal with these difficulties by exploiting the fact that HSIs generally “live” in a low-dimensional subspace and by tailoring the split augmented Lagrangian shrinkage algorithm (SALSA), which is an instance of the alternating direction method of multipliers (ADMM), to this optimization problem, by means of a convenient variable splitting. The spatial blur and the spectral linear operators linked, respectively, with the HSI and MSI acquisition processes are also estimated, and we obtain an effective algorithm that outperforms the state of the art, as illustrated in a series of experiments with simulated and real-life data.

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 "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"
    - "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-3 (induced by source paper "blind hyperspectral multispectral image fusion via graph laplacian regularization"):
  with members:
    - "constrained manifold learning for hyperspectral imagery visualization"
    - "hyperspectral pansharpening a review"
    - "projection onto the probability simplex an efficient algorithm with a simple proof and an application"

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 "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"
    - "hyperspectral and multispectral image fusion based on a sparse representation"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

Connected through hyperedge HE-6 (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"
    - "very deep convolutional networks for large scale image recognition"
    - "hyperspectral and multispectral image fusion based on a sparse representation"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "blind image fusion for hyperspectral imaging with the directional total variation"):
  with members:
    - "a haar wavelet based perceptual similarity index for image quality assessment"
    - "a survey of pansharpening methods with a new band decoupled variational model"
    - "nonparametric image registration of airborne lidar hyperspectral and photographic imagery of wooded landscapes"
    - "hyperspectral pansharpening a review"
    - "enhanced compressed sensing recovery with level set normals"

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"
    - "fast fusion of multi band images based on solving a sylvester equation"
    - "hyperspectral and multispectral image fusion based on a sparse representation"

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)