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: "boosting the accuracy of multispectral image pansharpening by learning a deep residual network"
  Abstract: In the field of multispectral (MS) and panchromatic image fusion (pansharpening), the impressive effectiveness of deep neural networks has recently been employed to overcome the drawbacks of the traditional linear models and boost the fusion accuracy. However, the existing methods are mainly based on simple and flat networks with relatively shallow architectures, which severely limits their performance. In this letter, the concept of residual learning is introduced to form a very deep convolutional neural network to make the full use of the high nonlinearity of the deep learning models. Through both quantitative and visual assessments on a large number of high-quality MS images from various sources, it is confirmed that the proposed model is superior to all the mainstream algorithms included in the comparison, and achieves the highest spatial–spectral unified accuracy.

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 image denoising employing a spatial spectral deep residual convolutional neural network"):
  with members:
    - "missing data reconstruction in remote sensing image with a unified spatial temporal spectral deep convolutional neural network"
    - "exploring models and data for remote sensing image caption generation"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"
    - "denoising hyperspectral image with non i i d noise structure"
    - "image restoration using convolutional auto encoders with symmetric skip connections"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "multispectral and hyperspectral image fusion by ms hs fusion net"):
  with members:
    - "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"
    - "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-4 (induced by source paper "psgan a generative adversarial network for remote sensing image pan sharpening"):
  with members:
    - "learning continuous face age progression a pyramid of gans"
    - "target adaptive cnn based pansharpening"
    - "remote sensing image fusion based on two stream fusion network"
    - "multi resolution data fusion for super resolution electron microscopy"
    - "accelerating the super resolution convolutional neural network"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (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"
    - "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"
    - "hyperspectral pansharpening a review"

Connected through hyperedge HE-6 (induced by source paper "target adaptive cnn based pansharpening"):
  with members:
    - "deep learning in remote sensing a review"
    - "land cover classification via multitemporal spatial data by deep recurrent neural networks"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "a survey of pansharpening methods with a new band decoupled variational model"
    - "accurate image super resolution using very deep convolutional networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"

Connected through hyperedge HE-7 (induced by source paper "hybrid noise removal in hyperspectral imagery with a spatial spectral gradient network"):
  with members:
    - "hyperspectral image denoising employing a spatial spectral deep residual convolutional neural network"
    - "target adaptive cnn based pansharpening"
    - "missing data reconstruction in remote sensing image with a unified spatial temporal spectral deep convolutional neural network"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "learning a dilated residual network for sar image despeckling"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "denoising hyperspectral image with non i i d noise structure"
    - "image restoration using convolutional auto encoders with symmetric skip connections"

Connected through hyperedge HE-8 (induced by source paper "missing data reconstruction in remote sensing image with a unified spatial temporal spectral deep convolutional neural network"):
  with members:
    - "embedding structured contour and location prior in siamesed fully convolutional networks for road detection"
    - "a joint convolutional neural networks and context transfer for street scenes labeling"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "high resolution image inpainting using multi scale neural patch synthesis"
    - "context encoders feature learning by inpainting"
    - "caffe convolutional architecture for fast feature embedding"

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)