human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "my camera can see through fences a deep learning approach for image de fencing"
  Abstract: In recent times, the availability of inexpensive image capturing devices such as smartphones/tablets has led to an exponential increase in the number of images/videos captured. However, sometimes the amateur photographer is hindered by fences in the scene which have to be removed after the image has been captured. Conventional approaches to image de-fencing suffer from inaccurate and non-robust fence detection apart from being limited to processing images of only static occluded scenes. In this paper, we propose a semi-automated de-fencing algorithm using a video of the dynamic scene. We use convolutional neural networks for detecting fence pixels. We provide qualitative as well as quantitative comparison results with existing lattice detection algorithms on the existing PSU NRT data set and a proposed challenging fenced image dataset. The inverse problem of fence removal is solved using split Bregman technique assuming total variation of the de-fenced image as the regularization constraint.

Connected through hyperedge HE-1 (induced by source paper "fully automated image de fencing using conditional generative adversarial networks"):
  with members:
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "an introduction to image synthesis with generative adversarial nets"
    - "generative image inpainting with contextual attention"
    - "perceptual losses for real time style transfer and super resolution"
    - "high resolution image inpainting using multi scale neural patch synthesis"
    - "generative adversarial text to image synthesis"
    - "semantic image inpainting with deep generative models"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "accurate and efficient video de fencing using convolutional neural networks and temporal information"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "stereo image de fencing using smartphones"
    - "deep learning based fence segmentation and removal from an image using a video sequence"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "very deep convolutional networks for large scale image recognition"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "deep learning based fence segmentation and removal from an image using a video sequence"):
  with members:
    - "epicflow edge preserving interpolation of correspondences for optical flow"
    - "matconvnet convolutional neural networks for matlab"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "video de fencing"

Connected through hyperedge HE-4 (induced by source paper "stereo image de fencing using smartphones"):
  with members:
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "video de fencing"

Question: Please tell me which class the center node 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)