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: "deep multi sensor lane detection"
  Abstract: Reliable and accurate lane detection has been a long-standing problem in the field of autonomous driving. In recent years, many approaches have been developed that use images (or videos) as input and reason in image space. In this paper we argue that accurate image estimates do not translate to precise 3D lane boundaries, which are the input required by modern motion planning algorithms. To address this issue, we propose a novel deep neural network that takes advantage of both LiDAR and camera sensors and produces very accurate estimates directly in 3D space. We demonstrate the performance of our approach on both highways and in cities, and show very accurate estimates in complex scenarios such as heavy traffic (which produces occlusion), fork, merges and intersections.

Connected through hyperedge HE-1 (induced by source paper "semi local 3d lane detection and uncertainty estimation"):
  with members:
    - "end to end lane detection through differentiable least squares fitting"
    - "learning lightweight lane detection cnns by self attention distillation"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "evaluating and calibrating uncertainty prediction in regression tasks"
    - "monoloco monocular 3d pedestrian localization and uncertainty estimation"
    - "a mixed classification regression framework for 3d pose estimation from 2d images"
    - "towards end to end lane detection an instance segmentation approach"
    - "3d lanenet end to end 3d multiple lane detection"

Connected through hyperedge HE-2 (induced by source paper "robust multi modality multi object tracking"):
  with members:
    - "deep continuous conditional random fields with asymmetric inter object constraints for online multi object tracking"
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "end to end learning of multi sensor 3d tracking by detection"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "combined image and world space tracking in traffic scenes"
    - "the perfect match 3d point cloud matching with smoothed densities"
    - "no blind spots full surround multi object tracking for autonomous vehicles using cameras lidars"

Connected through hyperedge HE-3 (induced by source paper "argoverse 3d tracking and forecasting with rich maps"):
  with members:
    - "the apolloscape open dataset for autonomous driving and its application"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "flownet3d learning scene flow in 3d point clouds"
    - "pointpillars fast encoders for object detection from point clouds"
    - "apollocar3d a large 3d car instance understanding benchmark for autonomous driving"

Connected through hyperedge HE-4 (induced by source paper "lane detection and classification using cascaded cnns"):
  with members:
    - "end to end lane detection through differentiable least squares fitting"
    - "learning to cluster for proposal free instance segmentation"
    - "towards end to end lane detection an instance segmentation approach"
    - "efficient road lane marking detection with deep learning"
    - "el gan embedding loss driven generative adversarial networks for lane detection"
    - "fast lidar based road detection using fully convolutional neural networks"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"
    - "spatial as deep spatial cnn for traffic scene understanding"

Connected through hyperedge HE-5 (induced by source paper "exploiting sparse semantic hd maps for self driving vehicle localization"):
  with members:
    - "semantic visual localization"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "find your way by observing the sun and other semantic cues"
    - "deep watershed transform for instance segmentation"
    - "torontocity seeing the world with a million eyes"
    - "pyramid scene parsing network"
    - "spatial transformer networks"
    - "netvlad cnn architecture for weakly supervised place recognition"

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)