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: "long term visual object tracking benchmark"
  Abstract: In this paper, we propose a new long video dataset (called Track Long and Prosper - TLP) and benchmark for visual object tracking. The dataset consists of 50 videos from real world scenarios, encompassing a duration of over 400 minutes (676K frames), making it more than 20 folds larger in average duration per sequence and more than 8 folds larger in terms of total covered duration, as compared to existing generic datasets for visual tracking. The proposed dataset paves a way to suitably assess long term tracking performance and possibly train better deep learning architectures (avoiding/reducing augmentation, which may not reflect realistic real world behavior). We benchmark the dataset on 17 state of the art trackers and rank them according to tracking accuracy and run time speeds. We further categorize the test sequences with different attributes and present a thorough quantitative and qualitative evaluation. Our most interesting observations are (a) existing short sequence benchmarks fail to bring out the inherent differences in tracking algorithms which widen up while tracking on long sequences and (b) the accuracy of most trackers abruptly drops on challenging long sequences, suggesting the potential need of research efforts in the direction of long term tracking.

Connected through hyperedge HE-1 (induced by source paper "a review of visual trackers and analysis of its application to mobile robot"):
  with members:
    - "long term tracking in the wild a benchmark"
    - "context aware deep feature compression for high speed visual tracking"
    - "adaptive correlation filters with long term and short term memory for object tracking"
    - "joint representation and truncated inference learning for correlation filter based tracking"
    - "a twofold siamese network for real time object tracking"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "good features to correlate for visual tracking"
    - "unveiling the power of deep tracking"

Connected through hyperedge HE-2 (induced by source paper "flow guided short term trackers with cascade detection for long term tracking"):
  with members:
    - "region proposal by guided anchoring"
    - "vision meets drones a challenge"
    - "long term tracking in the wild a benchmark"
    - "distractor aware siamese networks for visual object tracking"
    - "yolov3 an incremental improvement"
    - "siamrpn evolution of siamese visual tracking with very deep networks"
    - "now you see me evaluating performance in long term visual tracking"
    - "learning regression and verification networks for long term visual tracking"

Connected through hyperedge HE-3 (induced by source paper "globaltrack a simple and strong baseline for long term tracking"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "region proposal by guided anchoring"
    - "learning target oriented dual attention for robust rgb t tracking"
    - "skimming perusal tracking a framework for real time and robust long term tracking"
    - "atom accurate tracking by overlap maximization"
    - "long term tracking in the wild a benchmark"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "distractor aware siamese networks for visual object tracking"

Connected through hyperedge HE-4 (induced by source paper "performance evaluation methodology for long term visual object tracking"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "long term tracking in the wild a benchmark"
    - "now you see me evaluating performance in long term visual tracking"
    - "trackingnet a large scale dataset and benchmark for object tracking in the wild"
    - "tracking for half an hour"
    - "parallel tracking and verifying a framework for real time and high accuracy visual tracking"
    - "crest convolutional residual learning for visual tracking"
    - "need for speed a benchmark for higher frame rate object tracking"

Connected through hyperedge HE-5 (induced by source paper "exploring 3 r s of long term tracking re detection recovery and reliability"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "atom accurate tracking by overlap maximization"
    - "long term tracking in the wild a benchmark"
    - "yolov3 an incremental improvement"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "unveiling the power of deep tracking"
    - "siamrpn evolution of siamese visual tracking with very deep networks"

Connected through hyperedge HE-6 (induced by source paper "cdtb a color and depth visual object tracking dataset and benchmark"):
  with members:
    - "long term tracking in the wild a benchmark"
    - "depth masked discriminative correlation filter"
    - "object tracking by reconstruction with view specific discriminative correlation filters"
    - "now you see me evaluating performance in long term visual tracking"
    - "trackingnet a large scale dataset and benchmark for object tracking in the wild"
    - "learning regression and verification networks for long term visual tracking"
    - "fclt a fully correlational long term tracker"
    - "first person hand action benchmark with rgb d videos and 3d hand pose annotations"

Connected through hyperedge HE-7 (induced by source paper "model decay in long term tracking"):
  with members:
    - "long term tracking in the wild a benchmark"
    - "distractor aware siamese networks for visual object tracking"
    - "siamrpn evolution of siamese visual tracking with very deep networks"
    - "trackingnet a large scale dataset and benchmark for object tracking in the wild"
    - "mot16 a benchmark for multi object tracking"
    - "siamese instance search for tracking"
    - "fully convolutional siamese networks for object tracking"
    - "learning multi domain convolutional neural networks for visual tracking"

Connected through hyperedge HE-8 (induced by source paper "intra frame object tracking by deblatting"):
  with members:
    - "learning to extract a video sequence from a single motion blurred image"
    - "high speed tracking with multi kernel correlation filters"
    - "tracking for half an hour"
    - "learning blind motion deblurring"
    - "the world of fast moving objects"
    - "a novel performance evaluation methodology for single target trackers"
    - "performance measures and a data set for multi target multi camera tracking"
    - "learning spatially regularized correlation filters for 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)