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: "real time multiple object tracking a study on the importance of speed"
  Abstract: Multiple object tracking consists of detecting and identifying objects in video. In some applications, such as robotics and surveillance, it is desired that the tracking is performed in real-time. This poses a challenge in that it requires the algorithm to run as fast as the frame-rate of the video. Today's top performing tracking methods run at only a few frames per second, and can thus not be used in real-time. Further, when determining the speed of the tracker, it is common to not include the time it takes to detect objects. We argue that this way of measuring speed is not relevant for robotics or embedded systems, where the detecting of objects is done on the same machine as the tracking. We propose that one way of running a method in real-time is to not look at every frame, but skip frames to make the video have the same frame-rate as the tracking method. However, we believe that this will lead to decreased performance.In this project, we implement a multiple object tracker, following the tracking-by-detection paradigm, as an extension of an existing method. It works by modelling the movement of objects by solving the filtering problem, and associating detections with predicted new locations in new frames using the Hungarian algorithm. Three different similarity measures are used, which use the location and shape of the bounding boxes. Compared to other trackers on the MOTChallenge leaderboard, our method, referred to as C++SORT, is the fastest non-anonymous submission, while also achieving decent score on other metrics. By running our model on the Okutama-Action dataset, sampled at different frame-rates, we show that the performance is greatly reduced when running the model - including detecting objects - in real-time. In most metrics, the score is reduced by 50%, but in certain cases as much as 90%. We argue that this indicates that other, slower methods could not be used for tracking in real-time, but that more research is required specifically on this.

Connected through hyperedge HE-1 (induced by source paper "densecavoid real time navigation in dense crowds using anticipatory behaviors"):
  with members:
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "yolov3 an incremental improvement"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "porca modeling and planning for autonomous driving among many pedestrians"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "fully distributed multi robot collision avoidance via deep reinforcement learning for safe and efficient navigation in complex scenarios"

Connected through hyperedge HE-2 (induced by source paper "fooling detection alone is not enough first adversarial attack against multiple object tracking"):
  with members:
    - "tracking without bells and whistles"
    - "online multi object tracking with dual matching attention networks"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "seeing isn t believing practical adversarial attack against object detectors"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "yolov3 an incremental improvement"
    - "practical adversarial attack against object detector"

Connected through hyperedge HE-3 (induced by source paper "densepeds pedestrian tracking in dense crowds using front rvo and sparse features"):
  with members:
    - "roadtrack tracking road agents in dense and heterogeneous environments"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "a hybrid data association framework for robust online multi object tracking"
    - "fusion of head and full body detectors for multi object tracking"
    - "simple online and realtime tracking with a deep association metric"
    - "recurrent autoregressive networks for online multi object tracking"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"

Connected through hyperedge HE-4 (induced by source paper "online multi object tracking framework with the gmphd filter and occlusion group management"):
  with members:
    - "online multi object tracking with dual matching attention networks"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "fusion of head and full body detectors for multi object tracking"
    - "evolving boxes for fast vehicle detection"
    - "mot16 a benchmark for multi object tracking"
    - "learning by tracking siamese cnn for robust target association"
    - "high speed tracking with kernelized correlation filters"

Connected through hyperedge HE-5 (induced by source paper "deep similarity metric learning for real time pedestrian tracking"):
  with members:
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "in defense of the triplet loss for person re identification"
    - "sot for mot"
    - "simple online and realtime tracking with a deep association metric"
    - "end to end comparative attention networks for person re identification"
    - "mot16 a benchmark for multi object tracking"
    - "learning by tracking siamese cnn for robust target association"
    - "simple online and realtime tracking"

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)