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: "a two streamed network for estimating fine scaled depth maps from single rgb images"
  Abstract: Estimating depth from a single RGB image is an ill-posed and inherently ambiguous problem. State-of-the-art deep learning methods can now estimate accurate 2D depth maps, but when the maps are projected into 3D, they lack local detail and are often highly distorted. We propose a fast-to-train two-streamed CNN that predicts depth and depth gradients, which are then fused together into an accurate and detailed depth map. We also define a novel set loss over multiple images; by regularizing the estimation between a common set of images, the network is less prone to over-fitting and achieves better accuracy than competing methods. Experiments on the NYU Depth v2 dataset shows that our depth predictions are competitive with state-of-the-art and lead to faithful 3D projections.

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"
    - "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-2 (induced by source paper "std net structure preserving and topology adaptive deformation network for 3d reconstruction from a single image"):
  with members:
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "geometrics exploiting geometric structure for graph encoded objects"
    - "3dn 3d deformation network"
    - "sdm net deep generative network for structured deformable mesh"
    - "learning category specific mesh reconstruction from image collections"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "3d coded 3d correspondences by deep deformation"
    - "pixel2mesh generating 3d mesh models from single rgb images"

Connected through hyperedge HE-3 (induced by source paper "adversarial structure matching for structured prediction tasks"):
  with members:
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "taskonomy disentangling task transfer learning"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "regularizing deep networks by modeling and predicting label structure"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "deep value networks learn to evaluate and iteratively refine structured outputs"
    - "interpreting deep visual representations via network dissection"
    - "learning affinity via spatial propagation networks"

Connected through hyperedge HE-4 (induced by source paper "multidepth single image depth estimation via multi task regression and classification"):
  with members:
    - "auxnet auxiliary tasks enhanced semantic segmentation for automated driving"
    - "deep attention based classification network for robust depth prediction"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "segstereo exploiting semantic information for disparity estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "depth estimation via affinity learned with convolutional spatial propagation network"

Connected through hyperedge HE-5 (induced by source paper "exploiting temporal consistency for real time video depth estimation"):
  with members:
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "rethinking monocular depth estimation with adversarial training"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "deepvs a deep learning based video saliency prediction approach"
    - "revisiting single image depth estimation toward higher resolution maps with accurate object boundaries"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"

Connected through hyperedge HE-6 (induced by source paper "tw smnet deep multitask learning of tele wide stereo matching"):
  with members:
    - "amnet deep atrous multiscale stereo disparity estimation networks"
    - "deep ordinal regression network for monocular depth estimation"
    - "combining stereo disparity and optical flow for basic scene flow"
    - "pyramid stereo matching network"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "label refinement network for coarse to fine semantic segmentation"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"

Connected through hyperedge HE-7 (induced by source paper "peeking behind objects layered depth prediction from a single image"):
  with members:
    - "layer structured 3d scene inference via view synthesis"
    - "unsupervised learning of depth and ego motion from video"
    - "shape inpainting using 3d generative adversarial network and recurrent convolutional networks"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "factoring shape pose and layout from the 2d image of a 3d scene"
    - "semantic scene completion from a single depth image"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"

Connected through hyperedge HE-8 (induced by source paper "from big to small multi scale local planar guidance for monocular depth estimation"):
  with members:
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "learning depth from monocular videos using direct methods"
    - "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"

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)