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: "single view depth estimation from examples"
  Abstract: We describe a non-parametric, "example-based" method for estimating the depth of an object, viewed in a single photo. Our method consults a database of example 3D geometries, searching for those which look similar to the object in the photo. The known depths of the selected database objects act as shape priors which constrain the process of estimating the object's depth. We show how this process can be performed by optimizing a well defined target likelihood function, via a hard-EM procedure. We address the problem of representing the (possibly infinite) variability of viewing conditions with a finite (and often very small) example set, by proposing an on-the-fly example update scheme. We further demonstrate the importance of non-stationarity in avoiding misleading examples when estimating structured shapes. We evaluate our method and present both qualitative as well as quantitative results for challenging object classes. Finally, we show how this same technique may be readily applied to a number of related problems. These include the novel task of estimating the occluded depth of an object's backside and the task of tailoring custom fitting image-maps for input depths.

Connected through hyperedge HE-1 (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-2 (induced by source paper "extreme 3d face reconstruction looking past occlusions"):
  with members:
    - "expnet landmark free deep 3d facial expressions"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "end to end 3d face reconstruction with deep neural networks"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "sfsnet learning shape reflectance and illuminance of faces in the wild"
    - "on face segmentation face swapping and face perception"
    - "self supervised multi level face model learning for monocular reconstruction at over 250 hz"

Connected through hyperedge HE-3 (induced by source paper "faceposenet making a case for landmark free face alignment"):
  with members:
    - "l2 constrained softmax loss for discriminative face verification"
    - "kepler keypoint and pose estimation of unconstrained faces by learning efficient h cnn regressors"
    - "do we really need to collect millions of faces for effective face recognition"
    - "marr revisited 2d 3d alignment via surface normal prediction"
    - "a multi scale cascade fully convolutional network face detector"
    - "multi directional multi level dual cross patterns for robust face recognition"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"
    - "learning detailed face reconstruction from a single image"

Connected through hyperedge HE-4 (induced by source paper "extreme 3d face reconstruction seeing through occlusions"):
  with members:
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "end to end 3d face reconstruction with deep neural networks"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "self supervised multi level face model learning for monocular reconstruction at over 250 hz"
    - "semantic image inpainting with deep generative models"
    - "image to image translation with conditional adversarial networks"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"

Connected through hyperedge HE-5 (induced by source paper "dense correspondences across scenes and scales"):
  with members:
    - "depth extraction from video using non parametric sampling"

Connected through hyperedge HE-6 (induced by source paper "virtual view networks for object reconstruction"):
  with members:
    - "boundary cues for 3d object shape recovery"
    - "viewpoints and keypoints"
    - "simultaneous detection and segmentation"
    - "do convnets learn correspondence"
    - "depth transfer depth extraction from video using non parametric sampling"
    - "depth map prediction from a single image using a multi scale deep network"
    - "overfeat integrated recognition localization and detection using convolutional networks"

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)