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: "joint monocular 3d vehicle detection and tracking"
  Abstract: Vehicle 3D extents and trajectories are critical cues for predicting the future location of vehicles and planning future agent ego-motion based on those predictions. In this paper, we propose a novel online framework for 3D vehicle detection and tracking from monocular videos. The framework can not only associate detections of vehicles in motion over time, but also estimate their complete 3D bounding box information from a sequence of 2D images captured on a moving platform. Our method leverages 3D box depth-ordering matching for robust instance association and utilizes 3D trajectory prediction for re-identification of occluded vehicles. We also design a motion learning module based on an LSTM for more accurate long-term motion extrapolation. Our experiments on simulation, KITTI, and Argoverse datasets show that our 3D tracking pipeline offers robust data association and tracking. On Argoverse, our image-based method is significantly better for tracking 3D vehicles within 30 meters than the LiDAR-centric baseline methods.

Connected through hyperedge HE-1 (induced by source paper "objects as points"):
  with members:
    - "feature selective anchor free module for single shot object detection"
    - "scale aware trident networks for object detection"
    - "bottom up object detection by grouping extreme and center points"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "cornernet detecting objects as paired keypoints"
    - "yolov3 an incremental improvement"
    - "simple baselines for human pose estimation and tracking"
    - "acquisition of localization confidence for accurate object detection"

Connected through hyperedge HE-2 (induced by source paper "monoloco monocular 3d pedestrian localization and uncertainty estimation"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "capturing object detection uncertainty in multi layer grid maps"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "pifpaf composite fields for human pose estimation"
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "simple baselines for human pose estimation and tracking"
    - "multiposenet fast multi person pose estimation using pose residual network"

Connected through hyperedge HE-3 (induced by source paper "pointrgcn graph convolution networks for 3d vehicles detection refinement"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "from points to parts 3d object detection from point cloud with part aware and part aggregation network"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "leveraging shape completion for 3d siamese tracking"
    - "pixor real time 3d object detection from point clouds"
    - "patch refinement localized 3d object detection"
    - "voxel fpn multi scale voxel feature aggregation in 3d object detection from point clouds"
    - "fast point r cnn"

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)