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: "aperture supervision for monocular depth estimation"
  Abstract: We present a novel method to train machine learning algorithms to estimate scene depths from a single image, by using the information provided by a camera's aperture as supervision. Prior works use a depth sensor's outputs or images of the same scene from alternate viewpoints as supervision, while our method instead uses images from the same viewpoint taken with a varying camera aperture. To enable learning algorithms to use aperture effects as supervision, we introduce two differentiable aperture rendering functions that use the input image and predicted depths to simulate the depth-of-field effects caused by real camera apertures. We train a monocular depth estimation network end-to-end to predict the scene depths that best explain these finite aperture images as defocus-blurred renderings of the input all-in-focus image.

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"
    - "deep depth from defocus how can defocus blur improve 3d estimation using dense neural networks"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "synthetic defocus and look ahead autofocus for casual videography"):
  with members:
    - "sg fcn a motion and memory based deep learning model for video saliency detection"
    - "deeply supervised salient object detection with short connections"
    - "synthetic depth of field with a single camera mobile phone"
    - "deeplens shallow depth of field from a single image"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "scanner efficient video analysis at scale"
    - "tracking emerges by colorizing videos"

Connected through hyperedge HE-3 (induced by source paper "sterefo efficient image refocusing with stereo vision"):
  with members:
    - "real time self adaptive deep stereo"
    - "synthetic depth of field with a single camera mobile phone"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "deeplens shallow depth of field from a single image"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "learning monocular visual odometry with dense 3d mapping from dense 3d flow"

Connected through hyperedge HE-4 (induced by source paper "learning single camera depth estimation using dual pixels"):
  with members:
    - "wireless software synchronization of multiple distributed cameras"
    - "synthetic depth of field with a single camera mobile phone"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "digging into self supervised monocular depth estimation"
    - "a general and adaptive robust loss function"
    - "unsupervised learning of depth and ego motion from video"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-5 (induced by source paper "omnidepth dense depth estimation for indoors spherical panoramas"):
  with members:
    - "restricted deformable convolution based road scene semantic segmentation using surround view cameras"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "artistic style transfer for videos and spherical images"
    - "light weight pixel context encoders for image inpainting"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "the adobeindoornav dataset towards deep reinforcement learning based real world indoor robot visual navigation"
    - "learning high dynamic range from outdoor panoramas"
    - "active convolution learning the shape of convolution for image classification"

Connected through hyperedge HE-6 (induced by source paper "learning to synthesize motion blur"):
  with members:
    - "video enhancement with task oriented flow"
    - "synthetic depth of field with a single camera mobile phone"
    - "context aware synthesis for video frame interpolation"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "video frame interpolation via adaptive convolution"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "video frame interpolation via adaptive separable convolution"
    - "burst denoising with kernel prediction networks"

Connected through hyperedge HE-7 (induced by source paper "deep depth from defocus how can defocus blur improve 3d estimation using dense neural networks"):
  with members:
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "demon depth and motion network for learning monocular stereo"
    - "deep depth from focus"
    - "unsupervised monocular depth estimation with left right consistency"
    - "depth from a single image by harmonizing overcomplete local network predictions"
    - "densely connected convolutional networks"
    - "deeper depth prediction with fully convolutional residual networks"

Connected through hyperedge HE-8 (induced by source paper "a unified learning based framework for light field reconstruction from coded projections"):
  with members:
    - "learning light field reconstruction from a single coded image"
    - "spatial and angular resolution enhancement of light fields using convolutional neural networks"
    - "epinet a fully convolutional neural network using epipolar geometry for depth from light field images"
    - "learning to synthesize a 4d rgbd light field from a single image"
    - "unsupervised monocular depth estimation with left right consistency"
    - "learning based view synthesis for light field cameras"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "partial light field tomographic reconstruction from a fixed camera focal stack"

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)