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 "extreme view synthesis"):
  Abstract: We present Extreme View Synthesis, a solution for novel view extrapolation that works even when the number of input images is small--as few as two. In this context, occlusions and depth uncertainty are two of the most pressing issues, and worsen as the degree of extrapolation increases. We follow the traditional paradigm of performing depth-based warping and refinement, with a few key improvements. First, we estimate a depth probability volume, rather than just a single depth value for each pixel of the novel view. This allows us to leverage depth uncertainty in challenging regions, such as depth discontinuities. After using it to get an initial estimate of the novel view, we explicitly combine learned image priors and the depth uncertainty to synthesize a refined image with less artifacts. Our method is the first to show visually pleasing results for baseline magnifications of up to 30X.

Includes members:
  Member "neural rgb d sensing depth and uncertainty from a video camera":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "uncertainty depth estimation with gated images for 3d reconstruction")
  Member "local light field fusion practical view synthesis with prescriptive sampling guidelines":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "lighthouse predicting lighting volumes for spatially coherent illumination")
  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 "layer structured 3d scene inference via view synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "peelnet textured 3d reconstruction of human body using single view rgb image")
  Member "stereonet guided hierarchical refinement for real time edge aware depth prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "belief propagation reloaded learning bp layers for labeling problems")
  Member "megadepth learning single view depth prediction from internet photos":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "stereo magnification learning view synthesis using multiplane images":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "seeing the world in a bag of chips")

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)