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: "super resolution of sentinel 2 images learning a globally applicable deep neural network"
  Abstract: Abstract   The Sentinel-2 satellite mission delivers multi-spectral imagery with 13 spectral bands, acquired at three different spatial resolutions. The aim of this research is to super-resolve the lower-resolution (20 m and 60 m Ground Sampling Distance – GSD) bands to 10 m GSD, so as to obtain a complete data cube at the maximal sensor resolution. We employ a state-of-the-art convolutional neural network (CNN) to perform end-to-end upsampling, which is trained with data at lower resolution, i.e., from 40     →     20 m, respectively 360     →     60 m GSD. In this way, one has access to a virtually infinite amount of training data, by downsampling real Sentinel-2 images. We use data sampled globally over a wide range of geographical locations, to obtain a network that generalises across different climate zones and land-cover types, and can super-resolve arbitrary Sentinel-2 images without the need of retraining. In quantitative evaluations (at lower scale, where ground truth is available), our network, which we call DSen2, outperforms the best competing approach by almost 50% in RMSE, while better preserving the spectral characteristics. It also delivers visually convincing results at the full 10 m GSD.

Connected through hyperedge HE-1 (induced by source paper "temporal interpolation of geostationary satellite imagery with task specific optical flow"):
  with members:
    - "depth aware video frame interpolation"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "video frame interpolation via adaptive separable convolution"
    - "video frame synthesis using deep voxel flow"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "land use classification in remote sensing images by convolutional neural networks"
    - "deepsat a learning framework for satellite imagery"

Connected through hyperedge HE-2 (induced by source paper "optical flow for intermediate frame interpolation of multispectral geostationary satellite data"):
  with members:
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "video frame interpolation via adaptive convolution"
    - "video frame interpolation via adaptive separable convolution"
    - "deepsd generating high resolution climate change projections through single image super resolution"
    - "spatio temporal video autoencoder with differentiable memory"
    - "u net convolutional networks for biomedical image segmentation"
    - "land use classification in remote sensing images by convolutional neural networks"
    - "deepsat a learning framework for satellite imagery"

Connected through hyperedge HE-3 (induced by source paper "s3 a spectral spatial structure loss for pan sharpening networks"):
  with members:
    - "target adaptive cnn based pansharpening"
    - "decoupled weight decay regularization"
    - "enhanced deep residual networks for single image super resolution"
    - "densely connected convolutional networks"
    - "accurate image super resolution using very deep convolutional networks"
    - "deep residual learning for image recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-4 (induced by source paper "guided super resolution as pixel to pixel transformation"):
  with members:
    - "zero shot super resolution using deep internal learning"
    - "joint image filtering with deep convolutional networks"
    - "a deep primal dual network for guided depth super resolution"
    - "the fast bilateral solver"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "advances on cnn based super resolution of sentinel 2 images"):
  with members:
    - "target adaptive cnn based pansharpening"
    - "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"
    - "image super resolution using deep convolutional networks"

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)