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: "learning dual convolutional neural networks for low level vision"
  Abstract: In this paper, we propose a general dual convolutional neural network (DualCNN) for low-level vision problems, e.g., super-resolution, edge-preserving filtering, deraining and dehazing. These problems usually involve the estimation of two components of the target signals: structures and details. Motivated by this, our proposed DualCNN consists of two parallel branches, which respectively recovers the structures and details in an end-to-end manner. The recovered structures and details can generate the target signals according to the formation model for each particular application. The DualCNN is a flexible framework for low-level vision tasks and can be easily incorporated into existing CNNs. Experimental results show that the DualCNN can be effectively applied to numerous low-level vision tasks with favorable performance against the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "two phase hair image synthesis by self enhancing generative model"):
  with members:
    - "gif2video color dequantization and temporal interpolation of gif images"
    - "faceshop deep sketch based face image editing"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "hairnet single view hair reconstruction using convolutional neural networks"
    - "residual dense network for image super resolution"
    - "video inpainting by jointly learning temporal structure and spatial details"
    - "mganet a robust model for quality enhancement of compressed video"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-2 (induced by source paper "gradient information guided deraining with a novel network and adversarial training"):
  with members:
    - "rain removal in traffic surveillance does it matter"
    - "deep ordinal regression network for monocular depth estimation"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "density aware single image de raining using a multi stream dense network"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "fast single image rain removal via a deep decomposition composition network"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"

Connected through hyperedge HE-3 (induced by source paper "feratt facial expression recognition with attention net"):
  with members:
    - "fast and robust multiple colorchecker detection using deep convolutional neural networks"
    - "deep facial expression recognition a survey"
    - "enhanced deep residual networks for single image super resolution"
    - "zero shot super resolution using deep internal learning"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"
    - "identity mappings in deep residual networks"
    - "going deeper in facial expression recognition using deep neural networks"
    - "emergence of foveal image sampling from learning to attend in visual scenes"

Connected through hyperedge HE-4 (induced by source paper "fastderain a novel video rain streak removal method using directional gradient priors"):
  with members:
    - "non locally enhanced encoder decoder network for single image de raining"
    - "robust video content alignment and compensation for rain removal in a cnn framework"
    - "density aware single image de raining using a multi stream dense network"
    - "desnownet context aware deep network for snow removal"
    - "a novel nonconvex approach to recover the low tubal rank tensor data when t svd meets pssv"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "benchmarking single image dehazing and beyond"
    - "attentive generative adversarial network for raindrop removal from a single image"

Connected through hyperedge HE-5 (induced by source paper "deeply matting based dual generative adversarial network for image and document label supervision"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "deep image matting"
    - "language independent single document image super resolution using cnn for improved recognition"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-6 (induced by source paper "robust x ray sparse view phase tomography via hierarchical synthesis convolutional neural networks"):
  with members:
    - "non local low rank cube based tensor factorization for spectral ct reconstruction"
    - "phase recovery and holographic image reconstruction using deep learning in neural networks"
    - "learning to synthesize splitting and recombining low and high spatial frequencies for image recovery"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-7 (induced by source paper "learning to synthesize splitting and recombining low and high spatial frequencies for image recovery"):
  with members:
    - "phase recovery and holographic image reconstruction using deep learning in neural networks"
    - "lensless computational imaging through deep learning"
    - "deep learning microscopy"
    - "recurrent generative adversarial networks for proximal learning and automated compressive image recovery"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "understanding neural networks through deep visualization"

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)