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 pansharpening a review"
  Abstract: Pansharpening aims at fusing a panchromatic image with a multispectral one, to generate an image with the high spatial resolution of the former and the high spectral resolution of the latter. In the last decade, many algorithms have been presented in the literature for pansharpening using multispectral data. With the increasing availability of hyperspectral systems, these methods are now being adapted to hyperspectral images. In this work, we compare new pansharpening techniques designed for hyperspectral data with some of the state of the art methods for multispectral pansharpening, which have been adapted for hyperspectral data. Eleven methods from different classes (component substitution, multiresolution analysis, hybrid, Bayesian and matrix factorization) are analyzed. These methods are applied to three datasets and their effectiveness and robustness are evaluated with widely used performance indicators. In addition, all the pansharpening techniques considered in this paper have been implemented in a MATLAB toolbox that is made available to the community.

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"
    - "deep residual learning for image recognition"
    - "fast fusion of multi band images based on solving a sylvester equation"
    - "going deeper with convolutions"
    - "image super resolution using deep convolutional networks"

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"
    - "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-4 (induced by source paper "pirm2018 challenge on spectral image super resolution dataset and study"):
  with members:
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "accurate image super resolution using very deep convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "blind hyperspectral multispectral image fusion via graph laplacian regularization"):
  with members:
    - "constrained manifold learning for hyperspectral imagery visualization"
    - "a convex formulation for hyperspectral image superresolution via subspace based regularization"
    - "projection onto the probability simplex an efficient algorithm with a simple proof and an application"

Connected through hyperedge HE-6 (induced by source paper "restoration of pansharpened images by conditional filtering in the pca domain"):
  with members:
    - "a survey of pansharpening methods with a new band decoupled variational model"

Connected through hyperedge HE-7 (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-8 (induced by source paper "machine learning based hyperspectral image analysis a survey"):
  with members:
    - "a tutorial on modelling and inference in undirected graphical models for hyperspectral image analysis"
    - "going deeper with contextual cnn for hyperspectral image classification"
    - "a survey on object detection in optical remote sensing images"
    - "semantic segmentation using adversarial networks"
    - "unsupervised deep feature extraction for remote sensing image classification"
    - "deep residual learning for image recognition"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "sparse modeling for image and vision processing"

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)