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: "extended object tracking introduction overview and applications"
  Abstract: This article provides an elaborate overview of current research in extended object tracking. We provide a clear definition of the extended object tracking problem and discuss its delimitation to other types of object tracking. Next, different aspects of extended object modelling are extensively discussed. Subsequently, we give a tutorial introduction to two basic and well used extended object tracking approaches - the random matrix approach and the Kalman filter-based approach for star-convex shapes. The next part treats the tracking of multiple extended objects and elaborates how the large number of feasible association hypotheses can be tackled using both Random Finite Set (RFS) and Non-RFS multi-object trackers. The article concludes with a summary of current applications, where four example applications involving camera, X-band radar, light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are highlighted.

Connected through hyperedge HE-1 (induced by source paper "an rls based instantaneous velocity estimator for extended radar tracking"):
  with members:
    - "a survey of autonomous driving common practices and emerging technologies"

Connected through hyperedge HE-2 (induced by source paper "complexer yolo real time 3d object detection and tracking on semantic point clouds"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "yolov3 an incremental improvement"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "focal loss for dense object detection"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointfusion deep sensor fusion for 3d bounding box estimation"

Connected through hyperedge HE-3 (induced by source paper "decentralized poisson multi bernoulli filtering for vehicle tracking"):
  with members:
    - "poisson multi bernoulli mixture filter direct derivation and implementation"
    - "mono camera 3d multi object tracking using deep learning detections and pmbm filtering"
    - "robust distributed fusion with labeled random finite sets"
    - "generalized optimal sub pattern assignment metric"
    - "multisensor poisson multi bernoulli filter for joint target sensor state tracking"
    - "distributed fusion with multi bernoulli filter based on generalized covariance intersection"
    - "a metric on the space of finite sets of trajectories for evaluation of multi target tracking algorithms"
    - "marginal multi bernoulli filters rfs derivation of mht jipda and association based member"

Connected through hyperedge HE-4 (induced by source paper "real time prediction of automotive collision risk from monocular video"):
  with members:
    - "real time prediction of intermediate horizon automotive collision risk"
    - "multi agent imitation learning for driving simulation"
    - "learning depth from single images with deep neural network embedding focal length"
    - "imitating driver behavior with generative adversarial networks"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "depth map prediction from a single image using a multi scale deep network"

Connected through hyperedge HE-5 (induced by source paper "tracking the orientation and axes lengths of an elliptical extended object"):
  with members:
    - "second order extended kalman filter for extended object and group tracking"
    - "extended object tracking with random hypersurface models"

Connected through hyperedge HE-6 (induced by source paper "fusion of object tracking and dynamic occupancy grid map"):
  with members:
    - "environment perception framework fusing multi object tracking dynamic occupancy grid maps and digital maps"
    - "a random finite set approach for dynamic occupancy grid maps with real time application"

Connected through hyperedge HE-7 (induced by source paper "offline object extraction from dynamic occupancy grid map sequences"):
  with members:
    - "object detection on dynamic occupancy grid maps using deep learning and automatic label generation"
    - "fully convolutional neural networks for dynamic object detection in grid maps"

Connected through hyperedge HE-8 (induced by source paper "joint target detection and tracking in multipath environment a variational bayesian approach"):
  with members:
    - "a scalable algorithm for tracking an unknown number of targets using multiple sensors"
    - "multiple scan data association by convex variational inference"
    - "approximate evaluation of marginal association probabilities with belief propagation"

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)