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: "just in time reconstruction inpainting sparse maps using single view depth predictors as priors"
  Abstract: We present “just-in-time reconstruction” as realtime image-guided inpainting of a map with arbitrary scale and sparsity to generate a fully dense depth map for the image. In particular, our goal is to inpaint a sparse map - obtained from either a monocular visual SLAM system or a sparse sensor - using a single-view depth prediction network as a virtual depth sensor. We adopt a fairly standard approach to data fusion, to produce a fused depth map by performing inference over a novel fully-connected Conditional Random Field (CRF) which is parameterized by the input depth maps and their pixel-wise confidence weights. Crucially, we obtain the confidence weights that parameterize the CRF model in a data-dependent manner via Convolutional Neural Networks (CNNs) which are trained to model the conditional depth error distributions given each source of input depth map and the associated RGB image. Our CRF model penalises absolute depth error in its nodes and pairwise scale-invariant depth error in its edges, and the confidence-based fusion minimizes the impact of outlier input depth values on the fused result. We demonstrate the flexibility of our method by real-time inpainting of ORB-SLAM, Kinect, and LIDAR depth maps acquired both indoors and outdoors at arbitrary scale and varied amount of irregular sparsity.

Connected through hyperedge HE-1 (induced by source paper "cam convs camera aware multi scale convolutions for single view depth"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "deeptam deep tracking and mapping"
    - "deep attention based classification network for robust depth prediction"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep depth completion of a single rgb d image"
    - "deepmvs learning multi view stereopsis"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"

Connected through hyperedge HE-2 (induced by source paper "depth completion via deep basis fitting"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "depth coefficients for depth completion"
    - "dense depth posterior ddp from single image and sparse range"
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "voiced depth completion from inertial odometry and vision"
    - "noise aware unsupervised deep lidar stereo fusion"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"

Connected through hyperedge HE-3 (induced by source paper "empnet neural localisation and mapping using embedded memory points"):
  with members:
    - "deeptam deep tracking and mapping"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "traversing latent space using decision ferns"
    - "cream condensed real time models for depth prediction using convolutional neural networks"
    - "eng end to end neural geometry for robust depth and pose estimation using cnns"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"

Connected through hyperedge HE-4 (induced by source paper "image inpainting a review"):
  with members:
    - "local deep feature alignment for unsupervised dimension reduction"
    - "a novel approach for robust multi human action detection and recognition based on 3 dimentional convolutional neural networks"
    - "blind visual motif removal from a single image"
    - "adversarial inpainting of medical image modalities"
    - "deep hyperspectral prior denoising inpainting super resolution"
    - "pepsi fast and lightweight network for image inpainting"
    - "learning pyramid context encoder network for high quality image inpainting"
    - "foreground aware image inpainting"

Connected through hyperedge HE-5 (induced by source paper "sparse to continuous enhancing monocular depth estimation using occupancy maps"):
  with members:
    - "from big to small multi scale local planar guidance for monocular depth estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep depth completion of a single rgb d image"
    - "j mod 2 joint monocular obstacle detection and depth estimation"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "unsupervised learning of depth and ego motion from video"
    - "sparsity invariant cnns"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-6 (induced by source paper "environment reconstruction on depth images using generative adversarial networks"):
  with members:
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "semantic road layout understanding by generative adversarial inpainting"
    - "deep depth completion of a single rgb d image"
    - "generative image inpainting with contextual attention"
    - "semantic 3d occupancy mapping through efficient high order crfs"
    - "improved training of wasserstein gans"
    - "depth image inpainting improving low rank matrix completion with low gradient regularization"
    - "sparsity invariant cnns"

Connected through hyperedge HE-7 (induced by source paper "learned semantic multi sensor depth map fusion"):
  with members:
    - "pyramid stereo matching network"
    - "octnetfusion learning depth fusion from data"
    - "scancomplete large scale scene completion and semantic segmentation for 3d scans"
    - "cnn slam real time dense monocular slam with learned depth prediction"
    - "semantic scene completion from a single depth image"
    - "deeper depth prediction with fully convolutional residual networks"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "futuremapping 2 gaussian belief propagation for spatial ai"):
  with members:
    - "visual inertial mapping with non linear factor recovery"
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "futuremapping the computational structure of spatial ai systems"
    - "geometric deep learning going beyond euclidean data"
    - "distributed mapping with privacy and communication constraints lightweight algorithms and object based models"
    - "semanticfusion dense 3d semantic mapping with convolutional neural 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)