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: "deeplens shallow depth of field from a single image"
  Abstract: We aim to generate high resolution shallow depth-of-field (DoF) images from a single all-in-focus image with controllable focal distance and aperture size. To achieve this, we propose a novel neural network model comprised of a depth prediction module, a lens blur module, and a guided upsampling module. All modules are differentiable and are learned from data. To train our depth prediction module, we collect a dataset of 2462 RGB-D images captured by mobile phones with a dual-lens camera, and use existing segmentation datasets to improve border prediction. We further leverage a synthetic dataset with known depth to supervise the lens blur and guided upsampling modules. The effectiveness of our system and training strategies are verified in the experiments. Our method can generate high-quality shallow DoF images at high resolution, and produces significantly fewer artifacts than the baselines and existing solutions for single image shallow DoF synthesis. Compared with the iPhone portrait mode, which is a state-of-the-art shallow DoF solution based on a dual-lens depth camera, our method generates comparable results, while allowing for greater flexibility to choose focal points and aperture size, and is not limited to one capture setup.

Connected through hyperedge HE-1 (induced by source paper "3d ken burns effect from a single image"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "deferred neural rendering image synthesis using neural textures"
    - "transformable bottleneck networks"
    - "neural point based graphics"
    - "learning the depths of moving people by watching frozen people"
    - "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"
    - "local light field fusion practical view synthesis with prescriptive sampling guidelines"
    - "neural rerendering in the wild"

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"
    - "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"
    - "intel realsense stereoscopic depth cameras"

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"
    - "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"
    - "the perception distortion tradeoff"

Connected through hyperedge HE-4 (induced by source paper "web stereo video supervision for depth prediction from dynamic scenes"):
  with members:
    - "learning the depths of moving people by watching frozen people"
    - "deeptam deep tracking and mapping"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "deepmvs learning multi view stereopsis"
    - "learning monocular depth by distilling cross domain stereo networks"
    - "scalable dense non rigid structure from motion a grassmannian perspective"

Connected through hyperedge HE-5 (induced by source paper "high quality monocular depth estimation via transfer learning"):
  with members:
    - "deeptam deep tracking and mapping"
    - "detail preserving depth estimation from a single image using attention guided networks"
    - "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)