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: "coupled depth learning"
  Abstract: In this paper we propose a method for estimating depth from a single image using a coarse to fine approach. We argue that modeling the fine depth details is easier after a coarse depth map has been computed. We express a global (coarse) depth map of an image as a linear combination of a depth basis learned from training examples. The depth basis captures spatial and statistical regularities and reduces the problem of global depth estimation to the task of predicting the input-specific coefficients in the linear combination. This is formulated as a regression problem from a holistic representation of the image. Crucially, the depth basis and the regression function are {\bf coupled} and jointly optimized by our learning scheme. We demonstrate that this results in a significant improvement in accuracy compared to direct regression of depth pixel values or approaches learning the depth basis disjointly from the regression function. The global depth estimate is then used as a guidance by a local refinement method that introduces depth details that were not captured at the global level. Experiments on the NYUv2 and KITTI datasets show that our method outperforms the existing state-of-the-art at a considerably lower computational cost for both training and testing.

Connected through hyperedge HE-1 (induced by source paper "deep ordinal regression network for monocular depth estimation"):
  with members:
    - "densereg fully convolutional dense shape regression in the wild"
    - "depth estimation using structured light flow analysis of projected pattern flow on an object s surface"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "matterport3d learning from rgb d data in indoor environments"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "semi supervised deep learning for monocular depth map prediction"
    - "rethinking atrous convolution for semantic image segmentation"
    - "learning depth from single monocular images using deep convolutional neural fields"

Connected through hyperedge HE-2 (induced by source paper "megadepth learning single view depth prediction from internet photos"):
  with members:
    - "unsupervised learning of depth and ego motion from video"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "demon depth and motion network for learning monocular stereo"
    - "learning 3d object categories by looking around them"
    - "surface normals in the wild"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-3 (induced by source paper "surface normals in the wild"):
  with members:
    - "panoramic structure from motion via geometric relationship detection"
    - "rgbd datasets past present and future"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"
    - "unsupervised monocular depth estimation with left right consistency"
    - "marr revisited 2d 3d alignment via surface normal prediction"
    - "depth from a single image by harmonizing overcomplete local network predictions"
    - "deeper depth prediction with fully convolutional residual networks"
    - "playing for data ground truth from computer games"

Connected through hyperedge HE-4 (induced by source paper "depth2action exploring embedded depth for large scale action recognition"):
  with members:
    - "dap3d net where what and how actions occur in videos"
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "improving human activity recognition through ranking and re ranking"
    - "delving deeper into convolutional networks for learning video representations"
    - "human action recognition using factorized spatio temporal convolutional networks"
    - "beyond short snippets deep networks for video classification"
    - "depth extraction from videos using geometric context and occlusion boundaries"
    - "do less and achieve more training cnns for action recognition utilizing action images from the web"

Connected through hyperedge HE-5 (induced by source paper "single image depth perception in the wild"):
  with members:
    - "stacked hourglass networks for human pose estimation"
    - "a large dataset of object scans"
    - "holistically nested edge detection"
    - "monocular object instance segmentation and depth ordering with cnns"
    - "very deep convolutional networks for large scale image recognition"
    - "depth transfer depth extraction from video using non parametric sampling"
    - "depth map prediction from a single image using a multi scale deep network"
    - "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"

Connected through hyperedge HE-6 (induced by source paper "depth from a single image by harmonizing overcomplete local network predictions"):
  with members:
    - "return of the devil in the details delving deep into convolutional nets"
    - "designing deep networks for surface normal estimation"
    - "depth map prediction from a single image using a multi scale deep network"
    - "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"
    - "low level vision by consensus in a spatial hierarchy of regions"
    - "fully convolutional networks for semantic segmentation"
    - "depth extraction from video using non parametric sampling"

Connected through hyperedge HE-7 (induced by source paper "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"):
  with members:
    - "computing the stereo matching cost with a convolutional neural network"
    - "deep joint task learning for generic object extraction"
    - "convolutional feature masking for joint object and stuff segmentation"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "simultaneous detection and segmentation"

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)