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: "end to end image super resolution via deep and shallow convolutional networks"
  Abstract: One impressive advantage of convolutional neural networks (CNNs) is their ability to automatically learn feature representation from raw pixels, eliminating the need for hand-designed procedures. However, recent methods for single image super-resolution (SR) fail to maintain this advantage. They utilize CNNs in two decoupled steps, i.e., first upsampling the low resolution (LR) image to the high resolution (HR) size with hand-designed techniques (e.g., bicubic interpolation), and then applying CNNs on the upsampled LR image to reconstruct HR results. In this paper, we seek an alternative and propose a new image SR method, which jointly learns the feature extraction, upsampling and HR reconstruction modules, yielding a completely end-to-end trainable deep CNN. As opposed to existing approaches, the proposed method conducts upsampling in the latent feature space with filters that are optimized for the task of image SR. In addition, the HR reconstruction is performed in a multi-scale manner to simultaneously incorporate both short- and long-range contextual information, ensuring more accurate restoration of HR images. To facilitate network training, a new training approach is designed, which jointly trains the proposed deep network with a relatively shallow network, leading to faster convergence and more superior performance. The proposed method is extensively evaluated on widely adopted data sets and improves the performance of state-of-the-art methods with a considerable margin. Moreover, in-depth ablation studies are conducted to verify the contribution of different network designs to image SR, providing additional insights for future research.

Connected through hyperedge HE-1 (induced by source paper "point cloud super resolution with adversarial residual graph networks"):
  with members:
    - "splatnet sparse lattice networks for point cloud processing"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "so net self organizing network for point cloud analysis"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "residual dense network for image super resolution"
    - "image super resolution via dual state recurrent networks"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "pointsift a sift like network module for 3d point cloud semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "enhancing resolution of digital rock images with super resolution convolutional neural networks"):
  with members:
    - "ct image super resolution using 3d convolutional neural network"
    - "wide activation for efficient and accurate image super resolution"
    - "enhanced deep residual networks for single image super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "accurate image super resolution using very deep convolutional networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "multimedia super resolution via deep learning a survey"):
  with members:
    - "joint convolutional neural pyramid for depth map super resolution"
    - "learning a mixture of deep networks for single image super resolution"
    - "an analysis of deep neural network models for practical applications"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "failures of gradient based deep learning"
    - "enhanced deep residual networks for single image super resolution"
    - "fast and accurate image super resolution by deep cnn with skip connection and network in network"
    - "single image super resolution when model adaptation matters"

Connected through hyperedge HE-4 (induced by source paper "convolutional neural network based block up sampling for intra frame coding"):
  with members:
    - "accelerating the super resolution convolutional neural network"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "accurate image super resolution using very deep convolutional networks"
    - "deep residual learning for image recognition"
    - "deeply recursive convolutional network for image super resolution"
    - "learning deconvolution network for semantic segmentation"
    - "going deeper with convolutions"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "deepdpm dynamic population mapping via deep neural network"):
  with members:
    - "deepsd generating high resolution climate change projections through single image super resolution"
    - "a critical review of recurrent neural networks for sequence learning"
    - "deep residual learning for image recognition"
    - "deeply recursive convolutional network for image super resolution"
    - "deep networks for image super resolution with sparse prior"
    - "image super resolution using deep convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "attribute guided face generation using conditional cyclegan"):
  with members:
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "visual attribute transfer through deep image analogy"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "dualgan unsupervised dual learning for image to image translation"
    - "perceptual losses for real time style transfer and super resolution"
    - "invertible conditional gans for image editing"

Connected through hyperedge HE-7 (induced by source paper "photo realistic single image super resolution using a generative adversarial network"):
  with members:
    - "perceptual losses for real time style transfer and super resolution"
    - "full resolution image compression with recurrent neural networks"
    - "accelerating the super resolution convolutional neural network"
    - "combining markov random fields and convolutional neural networks for image synthesis"
    - "identity mappings in deep residual networks"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "semantic image inpainting with deep generative models"

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)