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: "deep cosine metric learning for person re identification"
  Abstract: Metric learning aims to construct an embedding where two extracted features corresponding to the same identity are likely to be closer than features from different identities. This paper presents a method for learning such a feature space where the cosine similarity is effectively optimized through a simple re-parametrization of the conventional softmax classification regime. At test time, the final classification layer can be stripped from the network to facilitate nearest neighbor queries on unseen individuals using the cosine similarity metric. This approach presents a simple alternative to direct metric learning objectives such as siamese networks that have required sophisticated pair or triplet sampling strategies in the past. The method is evaluated on two large-scale pedestrian re-identification datasets where competitive results are achieved overall. In particular, we achieve better generalization on the test set compared to a network trained with triplet loss.

Connected through hyperedge HE-1 (induced by source paper "gan based pose aware regulation for video based person re identification"):
  with members:
    - "multi pseudo regularized label for generated data in person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "deep metric learning by online soft mining and class aware attention"
    - "crossing generative adversarial networks for cross view person re identification"
    - "part aligned bilinear representations for person re identification"
    - "in defense of the triplet loss for person re identification"
    - "camera style adaptation for person re identification"
    - "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking"

Connected through hyperedge HE-2 (induced by source paper "aerial multi object tracking by detection using deep association networks"):
  with members:
    - "ev imo motion segmentation dataset and learning pipeline for event cameras"
    - "online multi object tracking with dual matching attention networks"
    - "cascade r cnn high quality object detection and instance segmentation"
    - "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"
    - "cornernet detecting objects as paired keypoints"
    - "event based moving object detection and tracking"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-3 (induced by source paper "predicting the future a jointly learnt model for action anticipation"):
  with members:
    - "coupled generative adversarial network for continuous fine grained action segmentation"
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "convolutional sequence to sequence model for human dynamics"
    - "multi level sequence gan for group activity recognition"
    - "discrimnet semi supervised action recognition from videos using generative adversarial networks"
    - "action anticipation by predicting future dynamic images"
    - "encouraging lstms to anticipate actions very early"
    - "visual forecasting by imitating dynamics in natural sequences"

Connected through hyperedge HE-4 (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"
    - "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"
    - "simple online and realtime tracking"

Connected through hyperedge HE-5 (induced by source paper "vehicle re identification an efficient baseline using triplet embedding"):
  with members:
    - "cityflow a city scale benchmark for multi target multi camera vehicle tracking and re identification"
    - "features for multi target multi camera tracking and re identification"
    - "sampling matters in deep embedding learning"
    - "in defense of the triplet loss for person re identification"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "working hard to know your neighbor s margins local descriptor learning loss"
    - "learning a discriminative null space for person re identification"

Connected through hyperedge HE-6 (induced by source paper "real time multi diver tracking and re identification for underwater human robot collaboration"):
  with members:
    - "cvpr19 tracking and detection challenge how crowded can it get"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "background segmentation for vehicle re identification"):
  with members:
    - "vehicle re identification an efficient baseline using triplet embedding"
    - "ram a region aware deep model for vehicle re identification"
    - "yolov3 an incremental improvement"
    - "in defense of the triplet loss for person re identification"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "pyramid scene parsing network"
    - "relay backpropagation for effective learning of deep convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "multiple human tracking using multi cues including primitive action features"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"
    - "action tubelet detector for spatio temporal action localization"
    - "simple online and realtime tracking with a deep association metric"
    - "okutama action an aerial view video dataset for concurrent human action detection"
    - "tube convolutional neural network t cnn for action detection in videos"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "ssd single shot multibox 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)