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: "tracklet association by online target specific metric learning and coherent dynamics estimation"
  Abstract: In this paper, we present a novel method based on online target-specific metric learning and coherent dynamics estimation for tracklet (track fragment) association by network flow optimization in long-term multi-person tracking. Our proposed framework aims to exploit appearance and motion cues to prevent identity switches during tracking and to recover missed detections. Furthermore, target-specific metrics (appearance cue) and motion dynamics (motion cue) are proposed to be learned and estimated online, i.e., during the tracking process. Our approach is effective even when such cues fail to identify or follow the target due to occlusions or object-to-object interactions. We also propose to learn the weights of these two tracking cues to handle the difficult situations, such as severe occlusions and object-to-object interactions effectively. Our method has been validated on several public datasets and the experimental results show that it outperforms several state-of-the-art tracking methods.

Connected through hyperedge HE-1 (induced by source paper "online multiple pedestrian tracking using deep temporal appearance matching association"):
  with members:
    - "cvpr19 tracking and detection challenge how crowded can it get"
    - "tracking without bells and whistles"
    - "online multi object tracking with dual matching attention networks"
    - "famnet joint learning of feature affinity and multi dimensional assignment for online multiple object tracking"
    - "deep continuous conditional random fields with asymmetric inter object constraints for online multi object tracking"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "trajectory factory tracklet cleaving and re connection by deep siamese bi gru for multiple object tracking"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"

Connected through hyperedge HE-2 (induced by source paper "end to end learning deep crf models for multi object tracking"):
  with members:
    - "online multi object tracking with instance aware tracker and dynamic model refreshment"
    - "online multi object tracking with dual matching attention networks"
    - "deep continuous conditional random fields with asymmetric inter object constraints for online multi object tracking"
    - "trajectory factory tracklet cleaving and re connection by deep siamese bi gru for multiple object tracking"
    - "multiple target tracking by learning feature representation and distance metric jointly"
    - "towards a principled integration of multi camera re identification and tracking through optimal bayes filters"
    - "in defense of the triplet loss for person re identification"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"

Connected through hyperedge HE-3 (induced by source paper "moana an online learned adaptive appearance model for robust multiple object tracking in 3d"):
  with members:
    - "deep continuous conditional random fields with asymmetric inter object constraints for online multi object tracking"
    - "a hybrid data association framework for robust online multi object tracking"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "online multi object tracking using cnn based single object tracker with spatial temporal attention mechanism"
    - "mot16 a benchmark for multi object tracking"
    - "online multi target tracking using recurrent neural networks"
    - "a multi cut formulation for joint segmentation and tracking of multiple objects"
    - "performance measures and a data set for multi target multi camera tracking"

Connected through hyperedge HE-4 (induced by source paper "machine learning methods for solving assignment problems in multi target tracking"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "combined image and world space tracking in traffic scenes"
    - "graph embedding techniques applications and performance a survey"
    - "carla an open urban driving simulator"
    - "learning optimal parameters for multi target tracking with contextual interactions"
    - "a hybrid data association framework for robust online multi object tracking"
    - "deep reinforcement learning a brief survey"
    - "fusion of head and full body detectors for multi object tracking"

Connected through hyperedge HE-5 (induced by source paper "tracklet association tracker an end to end learning based association approach for multi object tracking"):
  with members:
    - "fusion of head and full body detectors for multi object tracking"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "deep network flow for multi object tracking"
    - "mot16 a benchmark for multi object tracking"
    - "learning by tracking siamese cnn for robust target association"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-6 (induced by source paper "recurrent autoregressive networks for online multi object tracking"):
  with members:
    - "mot16 a benchmark for multi object tracking"
    - "learning by tracking siamese cnn for robust target association"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "simple online and realtime tracking"
    - "online multi target tracking using recurrent neural networks"
    - "poi multiple object tracking with high performance detection and appearance feature"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "multi class multi object tracking using changing point detection"

Connected through hyperedge HE-7 (induced by source paper "tracking the untrackable learning to track multiple cues with long term dependencies"):
  with members:
    - "deep learning of appearance models for online object tracking"
    - "mot16 a benchmark for multi object tracking"
    - "learning by tracking siamese cnn for robust target association"
    - "learning a discriminative null space for person re identification"
    - "a multi cut formulation for joint segmentation and tracking of multiple objects"
    - "performance measures and a data set for multi target multi camera tracking"
    - "learning to track at 100 fps with deep regression networks"
    - "temporal dynamic appearance modeling for online multi person tracking"

Connected through hyperedge HE-8 (induced by source paper "memory based online learning of deep representations from video streams"):
  with members:
    - "the extreme value machine"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "progressive neural networks"
    - "tracking the trackers an analysis of the state of the art in multiple object tracking"
    - "simple online and realtime tracking with a deep association metric"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "simple online and realtime tracking"
    - "poi multiple object tracking with high performance detection and appearance feature"

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)