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 "deltra deep light transport for projector camera systems"):
  Abstract: In projector-camera systems, light transport models the propagation from projector emitted radiance to camera-captured irradiance. In this paper, we propose the first end-to-end trainable solution named Deep Light Transport (DeLTra) that estimates radiometrically uncalibrated projector-camera light transport. DeLTra is designed to have two modules: DepthToAtrribute and ShadingNet. DepthToAtrribute explicitly learns rays, depth and normal, and then estimates rough Phong illuminations. Afterwards, the CNN-based ShadingNet renders photorealistic camera-captured image using estimated shading attributes and rough Phong illuminations. A particular challenge addressed by DeLTra is occlusion, for which we exploit epipolar constraint and propose a novel differentiable direct light mask. Thus, it can be learned end-to-end along with the other DeLTra modules. Once trained, DeLTra can be applied simultaneously to three projector-camera tasks: image-based relighting, projector compensation and depth/normal reconstruction. In our experiments, DeLTra shows clear advantages over previous arts with promising quality and meanwhile being practically convenient.

Includes members:
  Member "pro cam ssfm projector camera system for structure and spectral reflectance from motion":
    also connected through other hyperedges:
      (none)
  Member "compennet end to end full projector compensation":
    also connected through other hyperedges:
      (none)
  Member "kornia an open source differentiable computer vision library for pytorch":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "kaolin a pytorch library for accelerating 3d deep learning research")
  Member "self calibrating deep photometric stereo networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spline net sparse photometric stereo through lighting interpolation and normal estimation networks")
  Member "end to end projector photometric compensation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "illuminatedfocus vision augmentation using spatial defocusing via focal sweep eyeglasses and high speed projector")
  Member "neural inverse rendering for general reflectance photometric stereo":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep depth prior for multi view stereo")
  Member "a single shot per pose camera projector calibration system for imperfect planar targets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compennet end to end full projector compensation")
  Member "inverserendernet learning single image inverse rendering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lifting autoencoders unsupervised learning of a fully disentangled 3d morphable model using deep non rigid structure from motion")

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)