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: "detail preserving depth estimation from a single image using attention guided networks"
  Abstract: Convolutional Neural Networks have demonstrated superior performance on single image depth estimation in recent years. These works usually use stacked spatial pooling or strided convolution to get high-level information which are common practices in classification task. However, depth estimation is a dense prediction problem and low-resolution feature maps usually generate blurred depth map which is undesirable in application. In order to produce high quality depth map, say clean and accurate, we propose a network consists of a Dense Feature Extractor (DFE) and a Depth Map Generator (DMG). The DFE combines ResNet and dilated convolutions. It extracts multi-scale information from input image while keeping the feature maps dense. As for DMG, we use attention mechanism to fuse multi-scale features produced in DFE. Our Network is trained end-to-end and does not need any post-processing. Hence, it runs fast and can predict depth map in about 15 fps. Experiment results show that our method is competitive with the state-of-the-art in quantitative evaluation, but can preserve better structural details of the scene depth.

Connected through hyperedge HE-1 (induced by source paper "conf net toward high confidence dense 3d point cloud with error map prediction"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "generating and exploiting probabilistic monocular depth estimates"
    - "deep ordinal regression network for monocular depth estimation"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "propagating confidences through cnns for sparse data regression"
    - "deeplidar deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image"

Connected through hyperedge HE-2 (induced by source paper "conf net predicting depth completion error map for high confidence dense 3d point cloud"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "generating and exploiting probabilistic monocular depth estimates"
    - "deep ordinal regression network for monocular depth estimation"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "propagating confidences through cnns for sparse data regression"
    - "deeplidar deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image"

Connected through hyperedge HE-3 (induced by source paper "structure aware residual pyramid network for monocular depth estimation"):
  with members:
    - "deep ordinal regression network for monocular depth estimation"
    - "revisiting single image depth estimation toward higher resolution maps with accurate object boundaries"
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "deeper depth prediction with fully convolutional residual networks"
    - "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-4 (induced by source paper "high quality monocular depth estimation via transfer learning"):
  with members:
    - "deeptam deep tracking and mapping"
    - "deeplens shallow depth of field from a single image"
    - "deep ordinal regression network for monocular depth estimation"
    - "benchmark analysis of representative deep neural network architectures"
    - "deepmvs learning multi view stereopsis"
    - "taskonomy disentangling task transfer learning"
    - "noise2noise learning image restoration without clean data"
    - "structured attention guided convolutional neural fields for monocular depth estimation"

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)