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 "sterefo efficient image refocusing with stereo vision"):
  Abstract: Whether to attract viewer attention to a particular object, give the impression of depth or simply reproduce human-like scene perception, shallow depth of field images are used extensively by professional and amateur photographers alike. To this end, high quality optical systems are used in DSLR cameras to focus on a specific depth plane while producing visually pleasing bokeh. We propose a physically motivated pipeline to mimic this effect from all-in-focus stereo images, typically retrieved by mobile cameras. It is capable to change the focal plane a posteriori at 76 FPS on KITTI images to enable real-time applications. As our portmanteau suggests, SteReFo interrelates stereo-based depth estimation and refocusing efficiently. In contrast to other approaches, our pipeline is simultaneously fully differentiable, physically motivated, and agnostic to scene content. It also enables computational video focus tracking for moving objects in addition to refocusing of static images. We evaluate our approach on the publicly available datasets SceneFlow, KITTI, CityScapes and quantify the quality of architectural changes.

Includes members:
  Member "real time self adaptive deep stereo":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning monocular depth estimation infusing traditional stereo knowledge")
  Member "synthetic depth of field with a single camera mobile phone":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "3d ken burns effect from a single image")
  Member "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "uncertainty depth estimation with gated images for 3d reconstruction")
  Member "deeplens shallow depth of field from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "3d ken burns effect from a single image")
  Member "the 2018 pirm challenge on perceptual image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "perceptual image super resolution with progressive adversarial network")
  Member "df net unsupervised joint learning of depth and flow using cross task consistency":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "crdoco pixel level domain transfer with cross domain consistency")
  Member "learning monocular visual odometry with dense 3d mapping from dense 3d flow":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dymslam 4d dynamic scene reconstruction based on geometrical motion segmentation")

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)