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 "deep appearance maps"):
  Abstract: We propose a deep representation of appearance, i. e. the relation of color, surface orientation, viewer position, material and illumination. Previous approaches have used deep learning to extract classic appearance representations relating to reflectance model parameters (e. g. Phong) or illumination (e. g. HDR environment maps). We suggest to directly represent appearance itself as a network we call a deep appearance map (DAM). This is a 4D generalization over 2D reflectance maps, which held the view direction fixed. First, we show how a DAM can be learned from images or video frames and later be used to synthesize appearance, given new surface orientations and viewer positions. Second, we demonstrate how another network can be used to map from an image or video frames to a DAM network to reproduce this appearance, without using a lengthy optimization such as stochastic gradient descent (learning-to-learn). Finally, we generalize this to an appearance estimation-and-segmentation task, where we map from an image showing multiple materials to multiple networks reproducing their appearance, as well as per-pixel segmentation.

Includes members:
  Member "live intrinsic material estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pix2vex image to geometry reconstruction using a smooth differentiable renderer")
  Member "single image svbrdf capture with a rendering aware deep network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deltra deep light transport for projector camera systems")
  Member "lime live intrinsic material estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep multi class adversarial specularity removal")
  Member "deep shading convolutional neural networks for screen space shading":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deltra deep light transport for projector camera systems")
  Member "enhanced deep residual networks for single image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "joint material and illumination estimation from photo sets in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural rerendering in the wild")
  Member "material editing using a physically based rendering network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a method for estimating reflectance map and material using deep learning with synthetic dataset")
  Member "deep outdoor illumination estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "seeing the world in a bag of chips")

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)