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: "monocular depth estimation using multi scale continuous crfs as sequential deep networks"
  Abstract: Depth cues have been proved very useful in various computer vision and robotic tasks. This paper addresses the problem of monocular depth estimation from a single still image. Inspired by the effectiveness of recent works on multi-scale convolutional neural networks (CNN), we propose a deep model which fuses complementary information derived from multiple CNN side outputs. Different from previous methods using concatenation or weighted average schemes, the integration is obtained by means of continuous Conditional Random Fields (CRFs). In particular, we propose two different variations, one based on a cascade of multiple CRFs, the other on a unified graphical model. By designing a novel CNN implementation of mean-field updates for continuous CRFs, we show that both proposed models can be regarded as sequential deep networks and that training can be performed end-to-end. Through an extensive experimental evaluation, we demonstrate the effectiveness of the proposed approach and establish new state of the art results for the monocular depth estimation task on three publicly available datasets, i.e. NYUD-V2, Make3D and KITTI.

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"
    - "evaluation of cnn based single image depth estimation methods"
    - "fast end to end trainable guided filter"

Connected through hyperedge HE-2 (induced by source paper "progressive fusion for unsupervised binocular depth estimation using cycled networks"):
  with members:
    - "refine and distill exploiting cycle inconsistency and knowledge distillation for unsupervised monocular depth estimation"
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "adadepth unsupervised content congruent adaptation for depth estimation"
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "learning monocular depth by distilling cross domain stereo networks"

Connected through hyperedge HE-3 (induced by source paper "learning the depths of moving people by watching frozen people"):
  with members:
    - "learning rigidity in dynamic scenes with a moving camera for 3d motion field estimation"
    - "deeptam deep tracking and mapping"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "deepmvs learning multi view stereopsis"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "soccer on your tabletop"
    - "megadepth learning single view depth prediction from internet photos"

Connected through hyperedge HE-4 (induced by source paper "accurate monocular object detection via color embedded 3d reconstruction for autonomous driving"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep learning for generic object detection a survey"
    - "monogrnet a geometric reasoning network for monocular 3d object localization"
    - "deeplidar deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image"
    - "roi 10d monocular lifting of 2d detection to 6d pose and metric shape"

Connected through hyperedge HE-5 (induced by source paper "refine and distill exploiting cycle inconsistency and knowledge distillation for unsupervised monocular depth estimation"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "adadepth unsupervised content congruent adaptation for depth estimation"
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "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"
    - "learning monocular depth by distilling cross domain stereo networks"

Connected through hyperedge HE-6 (induced by source paper "task aware monocular depth estimation for 3d object detection"):
  with members:
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "towards instance level image to image translation"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "deep fitting degree scoring network for monocular 3d object detection"
    - "geometry aware symmetric domain adaptation for monocular depth estimation"
    - "triangulation learning network from monocular to stereo 3d object detection"
    - "monocular 3d object detection leveraging accurate proposals and shape reconstruction"
    - "deep attention based classification network for robust depth prediction"

Connected through hyperedge HE-7 (induced by source paper "structured coupled generative adversarial networks for unsupervised monocular depth estimation"):
  with members:
    - "refine and distill exploiting cycle inconsistency and knowledge distillation for unsupervised monocular depth estimation"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "adadepth unsupervised content congruent adaptation for depth estimation"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "learning monocular depth by distilling cross domain stereo networks"
    - "structured attention guided convolutional neural fields for monocular depth estimation"

Connected through hyperedge HE-8 (induced by source paper "structured modeling of joint deep feature and prediction refinement for salient object detection"):
  with members:
    - "bayesian loss for crowd count estimation with point supervision"
    - "saliency integration an arbitrator model"
    - "deeply supervised salient object detection with short connections"
    - "salient objects in clutter bringing salient object detection to the foreground"
    - "reverse attention for salient object detection"
    - "learning deep structured multi scale features using attention gated crfs for contour prediction"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "picanet learning pixel wise contextual attention for saliency detection"

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)