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 "5d light field synthesis from a monocular video"):
  Abstract: Commercially available light field cameras have difficulty in capturing 5D (4D + time) light field videos. They can only capture still light filed images or are excessively expensive for normal users to capture the light field video. To tackle this problem, we propose a deep learning-based method for synthesizing a light field video from a monocular video. We propose a new synthetic light field video dataset that renders photorealistic scenes using UnrealCV rendering engine because no light field dataset is available. The proposed deep learning framework synthesizes the light field video with a full set (9$\times$9) of sub-aperture images from a normal monocular video. The proposed network consists of three sub-networks, namely, feature extraction, 5D light field video synthesis, and temporal consistency refinement. Experimental results show that our model can successfully synthesize the light field video for synthetic and actual scenes and outperforms the previous frame-by-frame methods quantitatively and qualitatively. The synthesized light field can be used for conventional light field applications, namely, depth estimation, viewpoint change, and refocusing.

Includes members:
  Member "synthesizing a 4d spatio angular consistent light field from a single image":
    also connected through other hyperedges:
      (none)
  Member "local light field fusion practical view synthesis with prescriptive sampling guidelines":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "seeing the world in a bag of chips")
  Member "pushing the boundaries of view extrapolation with multiplane images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lighthouse predicting lighting volumes for spatially coherent illumination")
  Member "df net unsupervised joint learning of depth and flow using cross task consistency":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "crdoco pixel level domain transfer with cross domain consistency")
  Member "deepmvs learning multi view stereopsis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "learning blind video temporal consistency":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "crdoco pixel level domain transfer with cross domain consistency")
  Member "the unreasonable effectiveness of deep features as a perceptual metric":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "epinet a fully convolutional neural network using epipolar geometry for depth from light field images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "light field saliency detection with deep convolutional networks")

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)