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: "missing data reconstruction in remote sensing image with a unified spatial temporal spectral deep convolutional neural network"
  Abstract: Because of the internal malfunction of satellite sensors and poor atmospheric conditions such as thick cloud, the acquired remote sensing data often suffer from missing information, i.e., the data usability is greatly reduced. In this paper, a novel method of missing information reconstruction in remote sensing images is proposed. The unified spatial–temporal–spectral framework based on a deep convolutional neural network (CNN) employs a unified deep CNN combined with spatial–temporal–spectral supplementary information. In addition, to address the fact that most methods can only deal with a single missing information reconstruction task, the proposed approach can solve three typical missing information reconstruction tasks: 1) dead lines in Aqua Moderate Resolution Imaging Spectroradiometer band 6; 2) the Landsat Enhanced Thematic Mapper Plus scan line corrector-off problem; and 3) thick cloud removal. It should be noted that the proposed model can use multisource data (spatial, spectral, and temporal) as the input of the unified framework. The results of both simulated and real-data experiments demonstrate that the proposed model exhibits high effectiveness in the three missing information reconstruction tasks listed above.

Connected through hyperedge HE-1 (induced by source paper "deep learning inversion of electrical resistivity data"):
  with members:
    - "deep learning inversion of seismic data"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "deep convolutional neural network for inverse problems in imaging"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "hyperspectral image denoising employing a spatial spectral deep residual convolutional neural network"):
  with members:
    - "exploring models and data for remote sensing image caption generation"
    - "boosting the accuracy of multispectral image pansharpening by learning a deep residual network"
    - "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 "deep learning based cloud detection for medium and high resolution remote sensing images of different sensors"):
  with members:
    - "multi feature combined cloud and cloud shadow detection in gaofen 1 wide field of view imagery"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "accurate image super resolution using very deep convolutional networks"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "learning deconvolution network for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "temporal convolutional neural network for the classification of satellite image time series"):
  with members:
    - "learning spectral spatial temporal features via a recurrent convolutional neural network for change detection in multispectral imagery"
    - "deep learning for time series classification a review"
    - "3 d deep learning approach for remote sensing image classification"
    - "multi temporal land cover classification with sequential recurrent encoders"
    - "land cover classification via multitemporal spatial data by deep recurrent neural networks"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-5 (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"
    - "boosting the accuracy of multispectral image pansharpening by learning a deep residual 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"

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)