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: "fast and accurate image super resolution by deep cnn with skip connection and network in network"
  Abstract: We propose a highly efficient and faster Single Image Super-Resolution (SISR) model with Deep Convolutional neural networks (Deep CNN). Deep CNN have recently shown that they have a significant reconstruction performance on single-image super-resolution. Current trend is using deeper CNN layers to improve performance. However, deep models demand larger computation resources and is not suitable for network edge devices like mobile, tablet and IoT devices. Our model achieves state of the art reconstruction performance with at least 10 times lower calculation cost by Deep CNN with Residual Net, Skip Connection and Network in Network (DCSCN). A combination of Deep CNNs and Skip connection layers is used as a feature extractor for image features on both local and global area. Parallelized 1x1 CNNs, like the one called Network in Network, is also used for image reconstruction. That structure reduces the dimensions of the previous layer's output for faster computation with less information loss, and make it possible to process original images directly. Also we optimize the number of layers and filters of each CNN to significantly reduce the calculation cost. Thus, the proposed algorithm not only achieves the state of the art performance but also achieves faster and efficient computation. Code is available at this https URL

Connected through hyperedge HE-1 (induced by source paper "learned multi view texture super resolution"):
  with members:
    - "3d appearance super resolution with deep learning"
    - "deep learning for single image super resolution a brief review"
    - "fast and accurate single image super resolution via information distillation network"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimedia super resolution via deep learning a survey"
    - "a fully progressive approach to single image super resolution"
    - "residual dense network for image super resolution"
    - "deep network interpolation for continuous imagery effect transition"

Connected through hyperedge HE-2 (induced by source paper "veritatem dies aperit temporally consistent depth prediction enabled by a multi task geometric and semantic scene understanding approach"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "learning a discriminative feature network for semantic segmentation"
    - "deep depth completion of a single rgb d image"
    - "dynamic video segmentation network"
    - "low latency video semantic segmentation"
    - "generative image inpainting with contextual attention"
    - "skip connections eliminate singularities"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "deep attention based classification network for robust depth prediction"
    - "segstereo exploiting semantic information for disparity estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "pixel wise attentional gating for parsimonious pixel labeling"
    - "sparse and dense data with cnns depth completion and semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "multi task learning for automotive foggy scene understanding via domain adaptation to an illumination invariant representation"):
  with members:
    - "curriculum model adaptation with synthetic and real data for semantic foggy scene understanding"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "learning to adapt structured output space for semantic segmentation"
    - "semantic foggy scene understanding with synthetic data"
    - "incorporating luminance depth and color information by fusion based networks for semantic segmentation"
    - "skip connections eliminate singularities"
    - "demystifying neural style transfer"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "a hybrid ga pso method for evolving architecture and short connections of deep convolutional neural networks"):
  with members:
    - "a hybrid differential evolution approach to designing deep convolutional neural networks for image classification"
    - "automatically designing cnn architectures using genetic algorithm for image classification"
    - "evolving deep convolutional neural networks by variable length particle swarm optimization for image classification"
    - "skip connections eliminate singularities"
    - "learning transferable architectures for scalable image recognition"
    - "evolving deep convolutional neural networks for image classification"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "densely connected convolutional networks"

Connected through hyperedge HE-6 (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"
    - "single image super resolution when model adaptation matters"
    - "pixel recursive super resolution"

Connected through hyperedge HE-7 (induced by source paper "coconet a deep neural network for mapping pixel coordinates to color values"):
  with members:
    - "spatially adaptive image compression using a tiled deep network"
    - "optimizing the trade off between single stage and two stage object detectors using image difficulty prediction"
    - "cas cnn a deep convolutional neural network for image compression artifact suppression"
    - "how hard can it be estimating the difficulty of visual search in an image"
    - "learning to inpaint for image compression"
    - "full resolution image compression with recurrent neural networks"
    - "generic 3d convolutional fusion for image restoration"
    - "end to end optimized image compression"

Connected through hyperedge HE-8 (induced by source paper "channel splitting network for single mr image super resolution"):
  with members:
    - "single image super resolution via cascaded multi scale cross network"
    - "brain mri super resolution using 3d deep densely connected neural networks"
    - "residual dense network for image super resolution"
    - "aerial spectral super resolution using conditional adversarial networks"
    - "enhanced deep residual networks for single image super resolution"
    - "dual path networks"
    - "a survey on deep learning in medical image analysis"
    - "memnet a persistent memory network for image restoration"

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)