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: "cvpr19 tracking and detection challenge how crowded can it get"
  Abstract: Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for research. #R##N#The benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal to establish a standardized evaluation of multiple object tracking methods. The challenge focuses on multiple people tracking, since pedestrians are well studied in the tracking community, and precise tracking and detection has high practical relevance. Since the first release, MOT15, MOT16 and MOT17 have tremendously contributed to the community by introducing a clean dataset and precise framework to benchmark multi-object trackers. In this paper, we present our CVPR19 benchmark, consisting of 8 new sequences depicting very crowded challenging scenes. The benchmark will be presented at the 4th BMTT MOT Challenge Workshop at the Computer Vision and Pattern Recognition Conference (CVPR) 2019, and will evaluate the state-of-the-art in multiple object tracking whend handling extremely crowded scenarios.

Connected through hyperedge HE-1 (induced by source paper "panda a gigapixel level human centric video dataset"):
  with members:
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "collaborative global local networks for memory efficient segmentation of ultra high resolution images"
    - "widerperson a diverse dataset for dense pedestrian detection in the wild"
    - "group reidentification with multigrained matching and integration"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "vision meets drones a challenge"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "autofocus efficient multi scale inference"

Connected through hyperedge HE-2 (induced by source paper "virtual to real adaptation of pedestrian detectors for smart cities"):
  with members:
    - "yolov3 an incremental improvement"
    - "learning to detect and track visible and occluded body joints in a virtual world"
    - "citypersons a diverse dataset for pedestrian detection"
    - "training constrained deconvolutional networks for road scene semantic segmentation"
    - "mot16 a benchmark for multi object tracking"
    - "how far are we from solving pedestrian detection"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "driving in the matrix can virtual worlds replace human generated annotations for real world tasks"

Connected through hyperedge HE-3 (induced by source paper "online multiple pedestrian tracking using deep temporal appearance matching association"):
  with members:
    - "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"
    - "eliminating exposure bias and loss evaluation mismatch in multiple object tracking"

Connected through hyperedge HE-4 (induced by source paper "real time multi diver tracking and re identification for underwater human robot collaboration"):
  with members:
    - "deep cosine metric learning for person re identification"
    - "visual diver recognition for underwater human robot collaboration"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "person following by autonomous robots a categorical overview"
    - "enhancing underwater imagery using generative adversarial 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"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"

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)