human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "perceptual deep depth super resolution"):
  Abstract: RGBD images, combining high-resolution color and lower-resolution depth from various types of depth sensors, are increasingly common. One can significantly improve the resolution of depth maps by taking advantage of color information; deep learning methods make combining color and depth information particularly easy. However, fusing these two sources of data may lead to a variety of artifacts. If depth maps are used to reconstruct 3D shapes, e.g., for virtual reality applications, the visual quality of upsampled images is particularly important. The main idea of our approach is to measure the quality of depth map upsampling using renderings of resulting 3D surfaces. We demonstrate that a simple visual appearance-based loss, when used with either a trained CNN or simply a deep prior, yields significantly improved 3D shapes, as measured by a number of existing perceptual metrics. We compare this approach with a number of existing optimization and learning-based techniques.

Includes members:
  Member "deeply supervised depth map super resolution as novel view synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "viewsynth learning local features from depth using view synthesis")
  Member "recovering realistic texture in image super resolution by deep spatial feature transform":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "perceptual image super resolution with progressive adversarial network")
  Member "the unreasonable effectiveness of texture transfer for single image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "srobb targeted perceptual loss for single image super resolution")
  Member "image super resolution using very deep residual channel attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "depth estimation via affinity learned with convolutional spatial propagation network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "don t forget the past recurrent depth estimation from monocular video")
  Member "generative adversarial network based image super resolution using perceptual content losses":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "facial expression restoration based on improved graph convolutional networks")
  Member "self supervised sparse to dense self supervised depth completion from lidar and monocular camera":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "don t forget the past recurrent depth estimation from monocular video")
  Member "joint convolutional neural pyramid for depth map super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multimedia super resolution via deep learning a survey")

Question: Please tell me which class the source paper 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)