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: "doc deep occlusion estimation from a single image"
  Abstract: Recovering the occlusion relationships between objects is a fundamental human visual ability which yields important information about the 3D world. In this paper we propose a deep network architecture, called DOC, which acts on a single image, detects object boundaries and estimates the border ownership (i.e. which side of the boundary is foreground and which is background). We represent occlusion relations by a binary edge map, to indicate the object boundary, and an occlusion orientation variable which is tangential to the boundary and whose direction specifies border ownership by a left-hand rule, see Fig.1. We train two related deep convolutional neural networks, called DOC, which exploit local and non-local image cues to estimate this representation and hence recover occlusion relations. In order to train and test DOC we construct a large-scale instance occlusion boundary dataset using PASCAL VOC images, which we call the PASCAL instance occlusion dataset (PIOD). This contains 10,000 images and hence is two orders of magnitude larger than existing occlusion datasets for outdoor images. We test two variants of DOC on PIOD and on the BSDS occlusion dataset and show they outperform state-of-the-art methods typically by more than 5AP. Finally, we perform numerous experiments investigating multiple settings of DOC and transfer between BSDS and PIOD, which provides more insights for further study of occlusion estimation.

Connected through hyperedge HE-1 (induced by source paper "deep multicameral decoding for localizing unoccluded object instances from a single rgb image"):
  with members:
    - "fully dense unet for 2 d sparse photoacoustic tomography artifact removal"
    - "dual graph convolutional network for semantic segmentation"
    - "pointrend image segmentation as rendering"
    - "quantized densely connected u nets for efficient landmark localization"
    - "object segmentation in depth maps with one user click and a synthetically trained fully convolutional network"
    - "mvtec d2s densely segmented supermarket dataset"
    - "deep ordinal regression network for monocular depth estimation"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"

Connected through hyperedge HE-2 (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-3 (induced by source paper "occlusion shared and feature separated network for occlusion relationship reasoning"):
  with members:
    - "context constrained accurate contour extraction for occlusion edge detection"
    - "a novel multi layer framework for tiny obstacle discovery"
    - "deepflux for skeletons in the wild"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "dilated residual networks"
    - "understanding convolution for semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-4 (induced by source paper "grabar occlusion aware grabbing virtual objects in ar"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "3d hand shape and pose from images in the wild"
    - "pushing the envelope for rgb based dense 3d hand pose estimation via neural rendering"
    - "deep ordinal regression network for monocular depth estimation"
    - "deephps end to end estimation of 3d hand pose and shape by learning from synthetic depth"
    - "learning to infer the depth map of a hand from its color image"
    - "occlusion aware hand pose estimation using hierarchical mixture density network"
    - "depth estimation using structured light flow analysis of projected pattern flow on an object s surface"

Connected through hyperedge HE-5 (induced by source paper "ddnet dual path decoder network for occlusion relationship reasoning"):
  with members:
    - "context constrained accurate contour extraction for occlusion edge detection"
    - "bi directional cascade network for perceptual edge detection"
    - "occlusion shared and feature separated network for occlusion relationship reasoning"
    - "deep crisp boundaries from boundaries to higher level tasks"
    - "learning to predict crisp boundaries"
    - "richer convolutional features for edge detection"
    - "object contour detection with a fully convolutional encoder decoder network"
    - "holistically nested edge detection"

Connected through hyperedge HE-6 (induced by source paper "context constrained accurate contour extraction for occlusion edge detection"):
  with members:
    - "dilated residual networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "holistically nested edge detection"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "caffe convolutional architecture for fast feature embedding"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "doobnet deep object occlusion boundary detection from an image"):
  with members:
    - "semantic edge detection with diverse deep supervision"
    - "focal loss for dense object detection"
    - "richer convolutional features for edge detection"
    - "dilated residual networks"
    - "object contour detection with a fully convolutional encoder decoder network"
    - "holistically nested edge detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "pixel wise deep learning for contour detection"

Connected through hyperedge HE-8 (induced by source paper "visual concepts and compositional voting"):
  with members:
    - "normface l 2 hypersphere embedding for face verification"
    - "von mises fisher mixture model based deep learning application to face verification"
    - "amodal instance segmentation"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "amodal completion and size constancy in natural scenes"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "unsupervised learning of object semantic parts from internal states of cnns by population encoding"
    - "very deep convolutional networks for large scale image recognition"

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)