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 "single image reflection removal with physically based rendering"):
  Abstract: Recently, deep learning based single image reflection separation methods have been exploited widely. To benefit the learning approach, a large number of training image pairs (i.e., with and without reflections) were synthesized in various ways, yet they are away from a physically-based direction. In this paper, physically based rendering is used for faithfully synthesizing the required training images, and corresponding network structure is proposed. We utilize existing image data to estimate mesh, then physically simulate the depth-dependent light transportation between mesh, glass, and lens with path tracing. For guiding the separation better, we additionally consider a module of removing complicated ghosting and blurring glass-effects, which allows obtaining priori information before having the glass distortion. This module is easily accommodated within our approach, since that prior information can be physically generated by our rendering process. The proposed method considering the priori information as well as the existing posterior information is validated with various real reflection images, and is demonstrated to show visually pleasant and numerically better results compared to the state-of-theart techniques.

Includes members:
  Member "single image reflection separation with perceptual losses":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cooperative semantic segmentation and image restoration in adverse environmental conditions")
  Member "crrn multi scale guided concurrent reflection removal network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "single image reflection removal exploiting misaligned training data and network enhancements")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "a generic deep architecture for single image reflection removal and image smoothing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cooperative semantic segmentation and image restoration in adverse environmental conditions")
  Member "photo realistic single image super resolution using a generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "physically based rendering for indoor scene understanding using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "domain decluttering simplifying images to mitigate synthetic real domain shift and improve depth estimation")
  Member "single image depth perception in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "diversedepth affine invariant depth prediction using diverse data")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)