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: "depth extraction from video using non parametric sampling"
  Abstract: We describe a technique that automatically generates plausible depth maps from videos using non-parametric depth sampling. We demonstrate our technique in cases where past methods fail (non-translating cameras and dynamic scenes). Our technique is applicable to single images as well as videos. For videos, we use local motion cues to improve the inferred depth maps, while optical flow is used to ensure temporal depth consistency. For training and evaluation, we use a Kinect-based system to collect a large dataset containing stereoscopic videos with known depths. We show that our depth estimation technique outperforms the state-of-the-art on benchmark databases. Our technique can be used to automatically convert a monoscopic video into stereo for 3D visualization, and we demonstrate this through a variety of visually pleasing results for indoor and outdoor scenes, including results from the feature film Charade.

Connected through hyperedge HE-1 (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-2 (induced by source paper "learn stereo infer mono siamese networks for self supervised monocular depth estimation"):
  with members:
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "pixel wise attentional gating for parsimonious pixel labeling"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "digging into self supervised monocular depth estimation"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "extreme 3d face reconstruction seeing through occlusions"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-3 (induced by source paper "fastdepth fast monocular depth estimation on embedded systems"):
  with members:
    - "deeperlab single shot image parser"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep ordinal regression network for monocular depth estimation"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-4 (induced by source paper "meshdepth disconnected mesh based deep depth prediction"):
  with members:
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "deeptam deep tracking and mapping"
    - "sfmlearner learning monocular depth ego motion using meaningful geometric constraints"
    - "efficient semantic image segmentation with superpixel pooling"
    - "lego learning edge with geometry all at once by watching videos"
    - "digging into self supervised monocular depth estimation"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "sparse to dense depth prediction from sparse depth samples and a single image"

Connected through hyperedge HE-5 (induced by source paper "structure attentioned memory network for monocular depth estimation"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "adadepth unsupervised content congruent adaptation for depth estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "3d lmnet latent embedding matching for accurate and diverse 3d point cloud reconstruction from a single image"
    - "unsupervised cross modality domain adaptation of convnets for biomedical image segmentations with adversarial loss"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-6 (induced by source paper "semi supervised adversarial monocular depth estimation"):
  with members:
    - "deep ordinal regression network for monocular depth estimation"
    - "revisiting single image depth estimation toward higher resolution maps with accurate object boundaries"
    - "dual discriminator generative adversarial nets"
    - "unsupervised learning of depth and ego motion from video"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "semi supervised deep learning for monocular depth map prediction"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-7 (induced by source paper "an approximate shading model with detail decomposition for object relighting"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "neural face editing with intrinsic image disentangling"
    - "darn a deep adversial residual network for intrinsic image decomposition"
    - "learning detailed face reconstruction from a single image"
    - "learning diverse image colorization"
    - "unified depth prediction and intrinsic image decomposition from a single image via joint convolutional neural fields"
    - "direct intrinsics learning albedo shading decomposition by convolutional regression"

Connected through hyperedge HE-8 (induced by source paper "automatic scene inference for 3d object compositing"):
  with members:
    - "rendering synthetic objects into legacy photographs"

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)