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: "deep depth from defocus how can defocus blur improve 3d estimation using dense neural networks"
  Abstract: Depth estimation is of critical interest for scene understanding and accurate 3D reconstruction. Most recent approaches in depth estimation with deep learning exploit geometrical structures of standard sharp images to predict corresponding depth maps. However, cameras can also produce images with defocus blur depending on the depth of the objects and camera settings. Hence, these features may represent an important hint for learning to predict depth. In this paper, we propose a full system for single-image depth prediction in the wild using depth-from-defocus and neural networks. We carry out thorough experiments to test deep convolutional networks on real and simulated defocused images using a realistic model of blur variation with respect to depth. We also investigate the influence of blur on depth prediction observing model uncertainty with a Bayesian neural network approach. From these studies, we show that out-of-focus blur greatly improves the depth-prediction network performances. Furthermore, we transfer the ability learned on a synthetic, indoor dataset to real, indoor and outdoor images. For this purpose, we present a new dataset containing real all-focus and defocused images from a Digital Single-Lens Reflex (DSLR) camera, paired with ground truth depth maps obtained with an active 3D sensor for indoor scenes. The proposed approach is successfully validated on both this new dataset and standard ones as NYUv2 or Depth-in-the-Wild. Code and new datasets are available at this https URL

Connected through hyperedge HE-1 (induced by source paper "single image depth estimation trained via depth from defocus cues"):
  with members:
    - "deep ordinal regression network for monocular depth estimation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "dual attention network for scene segmentation"
    - "depth estimation using structured light flow analysis of projected pattern flow on an object s surface"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "aperture supervision for monocular depth estimation"

Connected through hyperedge HE-2 (induced by source paper "monster awakening the mono in stereo"):
  with members:
    - "single image depth estimation trained via depth from defocus cues"
    - "deep optics for monocular depth estimation and 3d object detection"
    - "ga net guided aggregation net for end to end stereo matching"
    - "monocular depth estimation a survey"
    - "learning depth with convolutional spatial propagation network"
    - "hierarchical discrete distribution decomposition for match density estimation"
    - "pyramid stereo matching network"
    - "deep depth inference using binocular and monocular cues"

Connected through hyperedge HE-3 (induced by source paper "deep optics for monocular depth estimation and 3d object detection"):
  with members:
    - "deep ordinal regression network for monocular depth estimation"
    - "monogrnet a geometric reasoning network for monocular 3d object localization"
    - "learning depth from single images with deep neural network embedding focal length"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-4 (induced by source paper "deepblindness fast blindness map estimation and blindness type classification for outdoor scene from single color image"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "learning to synthesize motion blur"
    - "haze density estimation via modeling of scattering coefficients of iso depth regions"
    - "deeplidar deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image"
    - "spatially varying blur detection based on multiscale fused and sorted transform coefficients of gradient magnitudes"
    - "a unified approach of multi scale deep and hand crafted features for defocus estimation"
    - "sparsity invariant cnns"
    - "joint transmission map estimation and dehazing using deep networks"

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)