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: "in defense of classical image processing fast depth completion on the cpu"
  Abstract: With the rise of data driven deep neural networks as a realization of universal function approximators, most research on computer vision problems has moved away from hand crafted classical image processing algorithms. This paper shows that with a well designed algorithm, we are capable of outperforming neural network based methods on the task of depth completion. The proposed algorithm is simple and fast, runs on the CPU, and relies only on basic image processing operations to perform depth completion of sparse LIDAR depth data. We evaluate our algorithm on the challenging KITTI depth completion benchmark, and at the time of submission, our method ranks first on the KITTI test server among all published methods. Furthermore, our algorithm is data independent, requiring no training data to perform the task at hand. The code written in Python will be made publicly available at this https URL

Connected through hyperedge HE-1 (induced by source paper "confidence guided stereo 3d object detection with split depth estimation"):
  with members:
    - "dsgn deep stereo geometry network for 3d object detection"
    - "canadian adverse driving conditions dataset"
    - "object centric stereo matching for 3d object detection"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "pv rcnn point voxel feature set abstraction for 3d object detection"
    - "task aware monocular depth estimation for 3d object detection"
    - "pseudo lidar accurate depth for 3d object detection in autonomous driving"
    - "scalability in perception for autonomous driving waymo open dataset"

Connected through hyperedge HE-2 (induced by source paper "general 3d room layout from a single view by render and compare"):
  with members:
    - "sharpnet fast and accurate recovery of occluding contours in monocular depth estimation"
    - "planercnn 3d plane detection and reconstruction from a single image"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-3 (induced by source paper "self supervised model adaptation for multimodal semantic segmentation"):
  with members:
    - "robot localization in floor plans using a room layout edge extraction network"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "multimodal interaction aware motion prediction for autonomous street crossing"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-4 (induced by source paper "pvn3d a deep point wise 3d keypoints voting network for 6dof pose estimation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "pix2pose pixel wise coordinate regression of objects for 6d pose estimation"
    - "centernet keypoint triplets for object detection"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "deep hough voting for 3d object detection in point clouds"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "deep object pose estimation for semantic robotic grasping of household objects"

Connected through hyperedge HE-5 (induced by source paper "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "deep attention based classification network for robust depth prediction"
    - "segstereo exploiting semantic information for disparity estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "pixel wise attentional gating for parsimonious pixel labeling"
    - "sparse and dense data with cnns depth completion and semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "cspn learning context and resource aware convolutional spatial propagation networks for depth completion"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "learning guided convolutional network for depth completion"
    - "deep high resolution representation learning for human pose estimation"
    - "learning depth with convolutional spatial propagation network"
    - "deep depth completion of a single rgb d image"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "structured attention guided convolutional neural fields for monocular depth estimation"

Connected through hyperedge HE-7 (induced by source paper "improving 3d object detection for pedestrians with virtual multi view synthesis orientation estimation"):
  with members:
    - "monocular 3d object detection leveraging accurate proposals and shape reconstruction"
    - "acquisition of localization confidence for accurate object detection"
    - "soccer on your tabletop"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "capnet continuous approximation projection for 3d point cloud reconstruction using 2d supervision"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"

Connected through hyperedge HE-8 (induced by source paper "dense depth posterior ddp from single image and sparse range"):
  with members:
    - "bilateral cyclic constraint and adaptive regularization for unsupervised monocular depth prediction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep depth completion of a single rgb d image"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "sbnet sparse blocks network for fast inference"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "propagating confidences through cnns for sparse data regression"

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)