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: "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"
  Abstract: As 3D movie viewing becomes mainstream and Virtual Reality (VR) market emerges, the demand for 3D contents is growing rapidly. Producing 3D videos, however, remains challenging. In this paper we propose to use deep neural networks for automatically converting 2D videos and images to stereoscopic 3D format. In contrast to previous automatic 2D-to-3D conversion algorithms, which have separate stages and need ground truth depth map as supervision, our approach is trained end-to-end directly on stereo pairs extracted from 3D movies. This novel training scheme makes it possible to exploit orders of magnitude more data and significantly increases performance. Indeed, Deep3D outperforms baselines in both quantitative and human subject evaluations.

Connected through hyperedge HE-1 (induced by source paper "predicting sharp and accurate occlusion boundaries in monocular depth estimation using displacement fields"):
  with members:
    - "sharpnet fast and accurate recovery of occluding contours in monocular depth estimation"
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "boundary cues for 3d object shape recovery"
    - "pattern affinitive propagation across depth surface normal and semantic segmentation"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "deep ordinal regression network for monocular depth estimation"
    - "monocular depth estimation using multi scale continuous crfs as sequential deep networks"
    - "evaluation of cnn based single image depth estimation methods"

Connected through hyperedge HE-2 (induced by source paper "single image depth estimation trained via depth from defocus cues"):
  with members:
    - "deep ordinal regression network for monocular depth estimation"
    - "deep depth from defocus how can defocus blur improve 3d estimation using dense neural networks"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "dual attention network for scene segmentation"
    - "depth estimation using structured light flow analysis of projected pattern flow on an object s surface"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"

Connected through hyperedge HE-3 (induced by source paper "3d ken burns effect from a single image"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "deferred neural rendering image synthesis using neural textures"
    - "transformable bottleneck networks"
    - "neural point based graphics"
    - "learning the depths of moving people by watching frozen people"
    - "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"
    - "local light field fusion practical view synthesis with prescriptive sampling guidelines"
    - "neural rerendering in the wild"

Connected through hyperedge HE-4 (induced by source paper "dynamonet dynamic action and motion network"):
  with members:
    - "visual dynamics stochastic future generation via layered cross convolutional networks"
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "self supervised learning of face representations for video face clustering"
    - "learning human optical flow"
    - "spatio temporal channel correlation networks for action classification"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "d3d distilled 3d networks for video action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-5 (induced by source paper "veritatem dies aperit temporally consistent depth prediction enabled by a multi task geometric and semantic scene understanding approach"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "learning a discriminative feature network for semantic segmentation"
    - "deep depth completion of a single rgb d image"
    - "dynamic video segmentation network"
    - "low latency video semantic segmentation"
    - "generative image inpainting with contextual attention"
    - "skip connections eliminate singularities"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "learning monocular depth estimation infusing traditional stereo knowledge"):
  with members:
    - "refine and distill exploiting cycle inconsistency and knowledge distillation for unsupervised monocular depth estimation"
    - "guided stereo matching"
    - "real time self adaptive deep stereo"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "segstereo exploiting semantic information for disparity estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"

Connected through hyperedge HE-7 (induced by source paper "bilateral cyclic constraint and adaptive regularization for unsupervised monocular depth prediction"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "geo supervised visual depth prediction"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "computer vision for autonomous vehicles problems datasets and state of the art"

Connected through hyperedge HE-8 (induced by source paper "learning depth from monocular videos using synthetic data a temporally consistent domain adaptation approach"):
  with members:
    - "learning the depths of moving people by watching frozen people"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "adadepth unsupervised content congruent adaptation for depth estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "t2net synthetic to realistic translation for solving single image depth estimation tasks"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "dual cnn models for unsupervised monocular depth estimation"

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)