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 "self supervised deep depth denoising"):
  Abstract: Depth perception is considered an invaluable source of information for various vision tasks. However, depth maps acquired using consumer-level sensors still suffer from non-negligible noise. This fact has recently motivated researchers to exploit traditional filters, as well as the deep learning paradigm, in order to suppress the aforementioned non-uniform noise, while preserving geometric details. Despite the effort, deep depth denoising is still an open challenge mainly due to the lack of clean data that could be used as ground truth. In this paper, we propose a fully convolutional deep autoencoder that learns to denoise depth maps, surpassing the lack of ground truth data. Specifically, the proposed autoencoder exploits multiple views of the same scene from different points of view in order to learn to suppress noise in a self-supervised end-to-end manner using depth and color information during training, yet only depth during inference. To enforce selfsupervision, we leverage a differentiable rendering technique to exploit photometric supervision, which is further regularized using geometric and surface priors. As the proposed approach relies on raw data acquisition, a large RGB-D corpus is collected using Intel RealSense sensors. Complementary to a quantitative evaluation, we demonstrate the effectiveness of the proposed self-supervised denoising approach on established 3D reconstruction applications. Code is avalable at this https URL

Includes members:
  Member "noise2self blind denoising by self supervision":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blur noise and compression robust generative adversarial networks")
  Member "a low cost flexible and portable volumetric capturing system":
    also connected through other hyperedges:
      (none)
  Member "interiornet mega scale multi sensor photo realistic indoor scenes dataset":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lighthouse predicting lighting volumes for spatially coherent illumination")
  Member "noise2void learning denoising from single noisy images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "blur noise and compression robust generative adversarial networks")
  Member "layer structured 3d scene inference via view synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "peelnet textured 3d reconstruction of human body using single view rgb image")
  Member "robust intrinsic and extrinsic calibration of rgb d cameras":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "intrinsic calibration of depth cameras for mobile robots using a radial laser scanner")
  Member "tackling 3d tof artifacts through learning and the flat dataset":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep end to end alignment and refinement for time of flight rgb d module")
  Member "deeptof off the shelf real time correction of multipath interference in time of flight imaging":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep end to end alignment and refinement for time of flight rgb d module")

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)