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 "content aware unsupervised deep homography estimation"):
  Abstract: Robust homography estimation between two images is a fundamental task which has been widely applied to various vision applications. Traditional feature based methods often detect image features and fit a homography according to matched features with RANSAC outlier removal. However, the quality of homography heavily relies on the quality of image features, which are prone to errors with respect to low light and low texture images. On the other hand, previous deep homography approaches either synthesize images for supervised learning or adopt aerial images for unsupervised learning, both ignoring the importance of depth disparities in homography estimation. Moreover, they treat the image content equally, including regions of dynamic objects and near-range foregrounds, which further decreases the quality of estimation. In this work, to overcome such problems, we propose an unsupervised deep homography method with a new architecture design. We learn a mask during the estimation to reject outlier regions. In addition, we calculate loss with respect to our learned deep features instead of directly comparing the image contents as did previously. Moreover, a comprehensive dataset is presented, covering both regular and challenging cases, such as poor textures and non-planar interferences. The effectiveness of our method is validated through comparisons with both feature-based and previous deep-based methods. Code will be soon available at Github.

Includes members:
  Member "gif2video color dequantization and temporal interpolation of gif images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "two phase hair image synthesis by self enhancing generative model")
  Member "frame recurrent video inpainting by robust optical flow inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "autoremover automatic object removal for autonomous driving videos")
  Member "semi supervised video salient object detection using pseudo labels":
    also connected through other hyperedges:
      (none)
  Member "two phase hair image synthesis by self enhancing generative model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semi supervised skin detection by network with mutual guidance")
  Member "learning raw image denoising with bayer pattern unification and bayer preserving augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "image restoration for under display camera")
  Member "semi supervised skin detection by network with mutual guidance":
    also connected through other hyperedges:
      (none)
  Member "video inpainting by jointly learning temporal structure and spatial details":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "do as i do transferring human motion and appearance between monocular videos with spatial and temporal constraints")
  Member "mganet a robust model for quality enhancement of compressed video":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "two phase hair image synthesis by self enhancing generative model")

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)