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: "got 10k a large high diversity benchmark for generic object tracking in the wild"
  Abstract: We introduce here a large tracking database that offers an unprecedentedly wide coverage of common moving objects in the wild, called GOT-10k. Specifically, GOT-10k is built upon the backbone of WordNet structure and it populates the majority of over 560 classes of moving objects and 87 motion patterns, magnitudes wider than the most recent similar-scale counterparts. The contributions of this paper are summarized in the following: (1) GOT-10k offers over 10,000 video segments with more than 1.5 million manually labeled bounding boxes, enabling unified training and stable evaluation of deep trackers. (2) GOT-10k is by far the first video trajectory dataset that uses the semantic hierarchy of WordNet to guide class population. (3) For the first time, GOT-10k introduces the one-shot protocol for tracker evaluation, where the training and test classes are zero-overlapped. The protocol avoids biased evaluation results towards familiar objects and it promotes generalization in tracker development. (4) We conduct extensive tracking experiments with 39 typical tracking algorithms on GOT-10k and analyze their results in this paper. (5) Finally, we develop a comprehensive platform for the tracking community that offers full-featured evaluation toolkits, an online evaluation server, and a responsive leaderboard. The annotations of GOT-10k's test data are kept private to avoid tuning parameters on it. The database, toolkits, evaluation server and baseline results are available at this http URL

Connected through hyperedge HE-1 (induced by source paper "siamese box adaptive network for visual tracking"):
  with members:
    - "learning discriminative model prediction for tracking"
    - "reppoints point set representation for object detection"
    - "gradnet gradient guided network for visual object tracking"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "spm tracker series parallel matching for real time visual object tracking"
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "feature selective anchor free module for single shot object detection"
    - "deeper and wider siamese networks for real time visual tracking"

Connected through hyperedge HE-2 (induced by source paper "watching the world go by representation learning from unlabeled videos"):
  with members:
    - "a simple framework for contrastive learning of visual representations"
    - "contrastive multiview coding"
    - "momentum contrast for unsupervised visual representation learning"
    - "learning the depths of moving people by watching frozen people"
    - "learning correspondence from the cycle consistency of time"
    - "data efficient image recognition with contrastive predictive coding"
    - "learning representations by maximizing mutual information across views"
    - "self supervised learning of pretext invariant representations"

Connected through hyperedge HE-3 (induced by source paper "deeply activated salient region for instance search"):
  with members:
    - "c mil continuation multiple instance learning for weakly supervised object detection"
    - "weakly supervised instance segmentation using class peak response"
    - "yolov3 an incremental improvement"
    - "instance search via instance level segmentation and feature representation"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "saliency weighted convolutional features for instance search"
    - "multiple instance detection network with online instance classifier refinement"
    - "mask r cnn"

Connected through hyperedge HE-4 (induced by source paper "state aware tracker for real time video object segmentation"):
  with members:
    - "siamfc towards robust and accurate visual tracking with target estimation guidelines"
    - "video object segmentation using space time memory networks"
    - "spatiotemporal cnn for video object segmentation"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "proposal tracking and segmentation pts a cascaded network for video object segmentation"
    - "ranet ranking attention network for fast video object segmentation"
    - "fast and accurate online video object segmentation via tracking parts"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"

Connected through hyperedge HE-5 (induced by source paper "an analysis of object representations in deep visual trackers"):
  with members:
    - "long term tracking in the wild a benchmark"
    - "target driven instance detection"
    - "siamrpn evolution of siamese visual tracking with very deep networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "towards accurate multi person pose estimation in the wild"
    - "detect to track and track to detect"
    - "learning to track at 100 fps with deep regression networks"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-6 (induced by source paper "skynet a hardware efficient method for object detection and tracking on embedded systems"):
  with members:
    - "accuracy vs efficiency achieving both through fpga implementation aware neural architecture search"
    - "skynet a champion model for dac sdc on low power object detection"
    - "single path nas device aware efficient convnet design"
    - "ternary hybrid neural tree networks for highly constrained iot applications"
    - "searching for mobilenetv3"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"
    - "resource constrained neural network architecture search"

Connected through hyperedge HE-7 (induced by source paper "unsupervised robust disentangling of latent characteristics for image synthesis"):
  with members:
    - "a two step disentanglement method"
    - "unsupervised part based disentangling of object shape and appearance"
    - "synthesizing images of humans in unseen poses"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "multimodal unsupervised image to image translation"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "mine mutual information neural estimation"
    - "cross modal deep variational hand pose estimation"

Connected through hyperedge HE-8 (induced by source paper "boltvos box level tracking for video object segmentation"):
  with members:
    - "4d generic video object proposals"
    - "deeper and wider siamese networks for real time visual tracking"
    - "siam r cnn visual tracking by re detection"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "rethinking imagenet pre training"
    - "vision meets drones a challenge"
    - "long term tracking in the wild a benchmark"
    - "siamese cascaded region proposal networks for real time visual 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)