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: "a baseline for 3d multi object tracking"
  Abstract: 3D multi-object tracking (MOT) is an essential component technology for many real-time applications such as autonomous driving or assistive robotics. However, recent works for 3D MOT tend to focus more on developing accurate systems giving less regard to computational cost and system complexity. In contrast, this work proposes a simple yet accurate real-time baseline 3D MOT system. We use an off-the-shelf 3D object detector to obtain oriented 3D bounding boxes from the LiDAR point cloud. Then, a combination of 3D Kalman filter and Hungarian algorithm is used for state estimation and data association. Although our baseline system is a straightforward combination of standard methods, we obtain the state-of-the-art results. To evaluate our baseline system, we propose a new 3D MOT extension to the official KITTI 2D MOT evaluation along with two new metrics. Our proposed baseline method for 3D MOT establishes new state-of-the-art performance on 3D MOT for KITTI, improving the 3D MOTA from 72.23 of prior art to 76.47. Surprisingly, by projecting our 3D tracking results to the 2D image plane and compare against published 2D MOT methods, our system places 2nd on the official KITTI leaderboard. Also, our proposed 3D MOT method runs at a rate of 214.7 FPS, 65 times faster than the state-of-the-art 2D MOT system. Our code is publicly available at this https URL

Connected through hyperedge HE-1 (induced by source paper "sequential forecasting of 100 000 points"):
  with members:
    - "joint 3d tracking and forecasting with graph neural network and diversity sampling"
    - "conditional invertible flow for point cloud generation"
    - "grip graph based interaction aware trajectory prediction"
    - "probabilistic reconstruction networks for 3d shape inference from a single image"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "human motion trajectory prediction a survey"
    - "pixor real time 3d object detection from point clouds"
    - "monocular 3d object detection with pseudo lidar point cloud"

Connected through hyperedge HE-2 (induced by source paper "joint 3d tracking and forecasting with graph neural network and diversity sampling"):
  with members:
    - "complexer yolo real time 3d object detection and tracking on semantic point clouds"
    - "grip graph based interaction aware trajectory prediction"
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "dual graph convolutional network for semantic segmentation"
    - "diverse trajectory forecasting with determinantal point processes"
    - "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"
    - "monocular 3d object detection with pseudo lidar point cloud"

Connected through hyperedge HE-3 (induced by source paper "3d object detection from lidar data using distance depended feature extraction"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "complexer yolo real time 3d object detection and tracking on semantic point clouds"
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "from points to parts 3d object detection from point cloud with part aware and part aggregation network"
    - "pixor real time 3d object detection from point clouds"
    - "range adaptation for 3d object detection in lidar"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "nuscenes a multimodal dataset for autonomous driving"

Connected through hyperedge HE-4 (induced by source paper "jrmot a real time 3d multi object tracker and a new large scale dataset"):
  with members:
    - "tracking without bells and whistles"
    - "track to reconstruct and reconstruct to track"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "fantrack 3d multi object tracking with feature association network"
    - "online multiple pedestrian tracking using deep temporal appearance matching association"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-5 (induced by source paper "probabilistic 3d multi object tracking for autonomous driving"):
  with members:
    - "class balanced grouping and sampling for point cloud 3d object detection"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-6 (induced by source paper "nuscenes a multimodal dataset for autonomous driving"):
  with members:
    - "how much real data do we actually need analyzing object detection performance using synthetic and real data"
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "starnet targeted computation for object detection in point clouds"
    - "spatially aware graph neural networks for relational behavior forecasting from sensor data"
    - "d 2 city a large scale dashcam video dataset of diverse traffic scenarios"
    - "disentangling monocular 3d object detection"
    - "physics based rendering for improving robustness to rain"
    - "the apolloscape open dataset for autonomous driving and its application"

Connected through hyperedge HE-7 (induced by source paper "forecasting time to collision from monocular video feasibility dataset and challenges"):
  with members:
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "learning spatio temporal features with two stream deep 3d cnns for lipreading"
    - "a visual neural network for robust collision perception in vehicle driving scenarios"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "yolov3 an incremental improvement"
    - "anticipating traffic accidents with adaptive loss and large scale incident db"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "learning spatio temporal representation with pseudo 3d residual networks"

Connected through hyperedge HE-8 (induced by source paper "future near collision prediction from monocular video feasibility dataset and challenges"):
  with members:
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "learning spatio temporal features with two stream deep 3d cnns for lipreading"
    - "a visual neural network for robust collision perception in vehicle driving scenarios"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "an evaluation of trajectory prediction approaches and notes on the trajnet benchmark"
    - "yolov3 an incremental improvement"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "learning spatio temporal representation with pseudo 3d residual 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)