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: "exploit the connectivity multi object tracking with trackletnet"
  Abstract: Multi-object tracking (MOT) is an important and practical task related to both surveillance systems and moving camera applications, such as autonomous driving and robotic vision. However, due to unreliable detection, occlusion and fast camera motion, tracked targets can be easily lost, which makes MOT very challenging. Most recent works treat tracking as a re-identification (Re-ID) task, but how to combine appearance and temporal features is still not well addressed. In this paper, we propose an innovative and effective tracking method called TrackletNet Tracker (TNT) that combines temporal and appearance information together as a unified framework. First, we define a graph model which treats each tracklet as a vertex. The tracklets are generated by appearance similarity with CNN features and intersection-over-union (IOU) with epipolar constraints to compensate camera movement between adjacent frames. Then, for every pair of two tracklets, the similarity is measured by our designed multi-scale TrackletNet. Afterwards, the tracklets are clustered into groups which represent individual object IDs. Our proposed TNT has the ability to handle most of the challenges in MOT, and achieve promising results on MOT16 and MOT17 benchmark datasets compared with other state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "rodnet object detection under severe conditions using vision radio cross modal supervision"):
  with members:
    - "video instance segmentation"
    - "the oxford radar robotcar dataset a radar extension to the oxford robotcar dataset"
    - "centernet keypoint triplets for object detection"
    - "tracking without bells and whistles"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "moana an online learned adaptive appearance model for robust multiple object tracking in 3d"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-2 (induced by source paper "unsupervised multiple person tracking using autoencoder based lifted multicuts"):
  with members:
    - "tracking without bells and whistles"
    - "famnet joint learning of feature affinity and multi dimensional assignment for online multiple object tracking"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"
    - "trajectory factory tracklet cleaving and re connection by deep siamese bi gru for multiple object tracking"
    - "unsupervised person re identification by deep learning tracklet association"
    - "fusion of head and full body detectors for multi object tracking"

Connected through hyperedge HE-3 (induced by source paper "object oriented video captioning with temporal graph and prior knowledge building"):
  with members:
    - "object aware aggregation with bidirectional temporal graph for video captioning"
    - "yolov3 an incremental improvement"
    - "reconstruction network for video captioning"
    - "simple to complex cross modal learning to rank"
    - "learning spatio temporal features with 3d residual networks for action recognition"
    - "dense captioning events in videos"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "mask r cnn"

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:
    - "a baseline for 3d multi object tracking"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "refinements in motion and appearance for online multi object tracking"):
  with members:
    - "online multi object tracking with instance aware tracker and dynamic model refreshment"
    - "tracking without bells and whistles"
    - "learning a neural solver for multiple object tracking"
    - "resolution invariant person re identification"
    - "online multi object tracking with dual matching attention networks"
    - "famnet joint learning of feature affinity and multi dimensional assignment for online multiple object tracking"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "tracklet association tracker an end to end learning based association approach for multi object tracking"

Connected through hyperedge HE-6 (induced by source paper "state aware re identification feature for multi target multi camera tracking"):
  with members:
    - "online multi object tracking with instance aware tracker and dynamic model refreshment"
    - "online multi object tracking with dual matching attention networks"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "multiple hypothesis tracking algorithm for multi target multi camera tracking with disjoint views"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "learning non uniform hypergraph for multi object tracking"
    - "features for multi target multi camera tracking and re identification"
    - "distractor aware siamese networks for visual object tracking"

Connected through hyperedge HE-7 (induced by source paper "eye in the sky drone based object tracking and 3d localization"):
  with members:
    - "vision meets drones a challenge"
    - "features for multi target multi camera tracking and re identification"
    - "eye in the sky real time drone surveillance system dss for violent individuals identification using scatternet hybrid deep learning network"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "soft nms improving object detection with one line of code"
    - "focal loss for dense object detection"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "detect to track and track to detect"

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)