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: "learning by tracking siamese cnn for robust target association"
  Abstract: This paper introduces a novel approach to the task of data association within the context of pedestrian tracking, by introducing a two-stage learning scheme to match pairs of detections. First, a Siamese convolutional neural network (CNN) is trained to learn descriptors encoding local spatio-temporal structures between the two input image patches, aggregating pixel values and optical flow information. Second, a set of contextual features derived from the position and size of the compared input patches are combined with the CNN output by means of a gradient boosting classifier to generate the final matching probability. This learning approach is validated by using a linear programming based multi-person tracker showing that even a simple and efficient tracker may outperform much more complex models when fed with our learned matching probabilities. Results on publicly available sequences show that our method meets state-of-the-art standards in multiple people tracking.

Connected through hyperedge HE-1 (induced by source paper "multiple object tracking by flowing and fusing"):
  with members:
    - "online multi object tracking with instance aware tracker and dynamic model refreshment"
    - "instance aware representation learning and association for online multi person tracking"
    - "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"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "towards multi object detection and tracking in urban scenario under uncertainties"

Connected through hyperedge HE-2 (induced by source paper "pack and detect fast object detection in videos using region of interest packing"):
  with members:
    - "yolov3 an incremental improvement"
    - "recovering from random pruning on the plasticity of deep convolutional neural networks"
    - "recurrent residual module for fast inference in videos"
    - "object detection in videos by short and long range object linking"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "fast yolo a fast you only look once system for real time embedded object detection in video"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"
    - "towards high performance video object detection"

Connected through hyperedge HE-3 (induced by source paper "unsupervised deep metric learning via auxiliary rotation loss"):
  with members:
    - "on mutual information maximization for representation learning"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "hardness aware deep metric learning"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "unsupervised pre training of image features on non curated data"
    - "data efficient image recognition with contrastive predictive coding"
    - "learning representations by maximizing mutual information across views"
    - "unsupervised embedding learning via invariant and spreading instance feature"

Connected through hyperedge HE-4 (induced by source paper "locality aware appearance metric for multi target multi camera tracking"):
  with members:
    - "multiple hypothesis tracking algorithm for multi target multi camera tracking with disjoint views"
    - "cityflow a city scale benchmark for multi target multi camera vehicle tracking and re identification"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "features for multi target multi camera tracking and re identification"
    - "improving online multiple object tracking with deep metric learning"
    - "multiple target tracking by learning feature representation and distance metric jointly"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"

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 "spatial temporal relation networks for multi object tracking"):
  with members:
    - "online multi object tracking with dual matching attention networks"
    - "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"
    - "non local neural networks"
    - "attention is all you need"
    - "relation networks for object detection"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-7 (induced by source paper "online multi object tracking with dual matching attention networks"):
  with members:
    - "focal loss for dense object detection"
    - "loss max pooling for semantic image segmentation"
    - "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"
    - "residual attention network for image classification"
    - "eco efficient convolution operators for tracking"
    - "mot16 a benchmark for multi object tracking"
    - "online multi target tracking using recurrent neural networks"

Connected through hyperedge HE-8 (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"

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.LG(Machine Learning)