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: "multiple object tracking a review"
  Abstract: Multiple Object Tracking (MOT) is an important computer vision task which has gained increasing attention due to its academic and commercial potential. Although different kinds of approaches have been proposed to tackle this problem, it still has many issues unsolved. For example, factors such as continuous appearance changes and severe occlusions result in difficulties for the task. In order to help the readers understand and learn this topic, we contribute a comprehensive and systematic review. We review the recent advances in various aspects about this topic and propose some interesting directions for future research. #R##N#To our best knowledge, there has not been any review about this topic in the community. The main contribution of this review is threefold: 1) All key aspects in the multiple object tracking system, including what scenarios the researchers are working on, how their work can be categorized, what needs to be considered when developing a MOT system and how to evaluate a MOT system, are discussed in a clear structure. This review work could not only provide researchers, especially new comers to the topic of MOT, a general understanding of the state-of-the-arts, but also help them to comprehend the aspects of a MOT system and the inter-connected aspects. 2) Instead of listing and summarizing individual publications, we categorize the approaches in the key aspects involved in a MOT system. In each aspect, the methods are divided into different groups and each group is discussed in details for the principles, advances and drawbacks. 3) We provide some potential directions with insights for MOT, which are still open issues and need more research efforts. This would be helpful for researchers to identify further interesting problems.

Connected through hyperedge HE-1 (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"
    - "exploit the connectivity multi object tracking with trackletnet"
    - "unsupervised person re identification by deep learning tracklet association"

Connected through hyperedge HE-2 (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-3 (induced by source paper "unsupervised deep tracking"):
  with members:
    - "real time mdnet"
    - "long term tracking in the wild a benchmark"
    - "a twofold siamese network for real time object tracking"
    - "end to end active object tracking and its real world deployment via reinforcement learning"
    - "deep attentive tracking via reciprocative learning"
    - "vital visual tracking via adversarial learning"
    - "learning dynamic memory networks for object tracking"
    - "trackingnet a large scale dataset and benchmark for object tracking in the wild"

Connected through hyperedge HE-4 (induced by source paper "spin a high speed high resolution vision dataset for tracking and action recognition in ping pong"):
  with members:
    - "stochastic prediction of multi agent interactions from partial observations"
    - "dynamonet dynamic action and motion network"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "tracking noisy targets a review of recent object tracking approaches"
    - "a proposal based solution to spatio temporal action detection in untrimmed videos"
    - "video action transformer network"
    - "representation flow for action recognition"
    - "a 2 nets double attention networks"

Connected through hyperedge HE-5 (induced by source paper "tracking without bells and whistles"):
  with members:
    - "online multi object tracking with dual matching attention networks"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "features for multi target multi camera tracking and re identification"
    - "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"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "in defense of the triplet loss for person re identification"
    - "an implementation of faster rcnn with study for region sampling"

Connected through hyperedge HE-6 (induced by source paper "multiple object tracking with motion and appearance cues"):
  with members:
    - "how to train your deep multi object tracker"
    - "deepmot a differentiable framework for training multiple object trackers"
    - "vision meets drones a challenge"
    - "yolov3 an incremental improvement"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "focal loss for dense object detection"
    - "cascade r cnn delving into high quality object detection"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"

Connected through hyperedge HE-7 (induced by source paper "intelligent intersection two stream convolutional networks for real time near accident detection in traffic video"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "deep cosine metric learning for person re identification"
    - "anticipating traffic accidents with adaptive loss and large scale incident db"
    - "drive video analysis for the detection of traffic near miss incidents"
    - "cadp a novel dataset for cctv traffic camera based accident analysis"
    - "in defense of the triplet loss for person re identification"
    - "single shot text detector with regional attention"
    - "simple online and realtime tracking with a deep association metric"

Connected through hyperedge HE-8 (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"

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)