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: "simultaneously color depth super resolution with conditional generative adversarial network"
  Abstract: Recently, Generative Adversarial Network (GAN) has been found wide applications in style transfer, image-to-image translation and image super-resolution. In this paper, a color-depth conditional GAN is proposed to concurrently resolve the problems of depth super-resolution and color super-resolution in 3D videos. Firstly, given the low-resolution depth image and low-resolution color image, a generative network is proposed to leverage mutual information of color image and depth image to enhance each other in consideration of the geometry structural dependency of color-depth image in the same scene. Secondly, three loss functions, including data loss, total variation loss, and 8-connected gradient difference loss are introduced to train this generative network in order to keep generated images close to the real ones, in addition to the adversarial loss. Experimental results demonstrate that the proposed approach produces high-quality color image and depth image from low-quality image pair, and it is superior to several other leading methods. Besides, we use the same neural network framework to resolve the problem of image smoothing and edge detection at the same time.

Connected through hyperedge HE-1 (induced by source paper "perceptual deep depth super resolution"):
  with members:
    - "deeply supervised depth map super resolution as novel view synthesis"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "the unreasonable effectiveness of texture transfer for single image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "generative adversarial network based image super resolution using perceptual content losses"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "joint convolutional neural pyramid for depth map super resolution"

Connected through hyperedge HE-2 (induced by source paper "multiple description convolutional neural networks for image compression"):
  with members:
    - "cas cnn a deep convolutional neural network for image compression artifact suppression"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "learning a virtual codec based on deep convolutional neural network to compress image"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep generative adversarial compression artifact removal"
    - "accurate image super resolution using very deep convolutional networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "combinatorial message sharing and a new achievable region for multiple descriptions"

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)