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: "depth map completion by jointly exploiting blurry color images and sparse depth maps"
  Abstract: We aim at predicting a complete and high-resolution depth map from incomplete, sparse and noisy depth measurements. Existing methods handle this problem either by exploiting various regularizations on the depth maps directly or resorting to learning based methods. When the corresponding color images are available, the correlation between the depth maps and the color images are used to improve the completion performance, assuming the color images are clean and sharp. However, in real world dynamic scenes, color images are often blurry due to the camera motion and the moving objects in the scene. In this paper, we propose to tackle the problem of depth map completion by jointly exploiting the blurry color image sequences and the sparse depth map measurements, and present an energy minimization based formulation to simultaneously complete the depth maps, estimate the scene flow and deblur the color images. Our experimental evaluations on both outdoor and indoor scenarios demonstrate the state-of-the-art performance of our approach.

Connected through hyperedge HE-1 (induced by source paper "unsupervised enhancement of real world depth images using tri cycle gan"):
  with members:
    - "s flow gan"
    - "rethinking lossy compression the rate distortion perception tradeoff"
    - "generative adversarial networks for extreme learned image compression"
    - "deep depth completion of a single rgb d image"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "deeplidar deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image"
    - "sparse to dense depth prediction from sparse depth samples and a single image"

Connected through hyperedge HE-2 (induced by source paper "predicting unobserved space for planning via depth map augmentation"):
  with members:
    - "depth coefficients for depth completion"
    - "fusion of stereo and still monocular depth estimates in a self supervised learning context"
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "deep depth completion of a single rgb d image"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "safe local exploration for replanning in cluttered unknown environments for microaerial vehicles"

Connected through hyperedge HE-3 (induced by source paper "high frame rate video reconstruction based on an event camera"):
  with members:
    - "asynchronous spatial image convolutions for event cameras"
    - "semi dense 3d reconstruction with a stereo event camera"
    - "computing spatial image convolutions for event cameras"
    - "learning to extract a video sequence from a single motion blurred image"
    - "scale recurrent network for deep image deblurring"
    - "phase only image based kernel estimation for single image blind deblurring"
    - "bringing a blurry frame alive at high frame rate with an event camera"
    - "photorealistic image reconstruction from hybrid intensity and event based sensor"

Connected through hyperedge HE-4 (induced by source paper "stochastic attraction repulsion embedding for large scale image localization"):
  with members:
    - "cplanet enhancing image geolocalization by combinatorial partitioning of maps"
    - "sampling matters in deep embedding learning"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "deep metric learning with angular loss"
    - "revisiting im2gps in the deep learning era"
    - "end to end learning of deep visual representations for image retrieval"
    - "deep image retrieval learning global representations for image search"
    - "planet photo geolocation with convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "bringing a blurry frame alive at high frame rate with an event camera"):
  with members:
    - "asynchronous spatial image convolutions for event cameras"
    - "semi dense 3d reconstruction with a stereo event camera"
    - "learning to extract a video sequence from a single motion blurred image"
    - "continuous time intensity estimation using event cameras"
    - "asynchronous photometric feature tracking using events and frames"
    - "photorealistic image reconstruction from hybrid intensity and event based sensor"
    - "ev flownet self supervised optical flow estimation for event based cameras"
    - "bringing alive blurred moments"

Connected through hyperedge HE-6 (induced by source paper "phase only image based kernel estimation for single image blind deblurring"):
  with members:
    - "single image deblurring and camera motion estimation with depth map"
    - "learning a discriminative prior for blind image deblurring"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "simultaneous stereo video deblurring and scene flow estimation"
    - "stereo video deblurring"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "from motion blur to motion flow a deep learning solution for removing heterogeneous motion blur"

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)