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: "skimming perusal tracking a framework for real time and robust long term tracking"
  Abstract: Compared with traditional short-term tracking, long-term tracking poses more challenges and is much closer to realistic applications. However, few works have been done and their performance have also been limited. In this work, we present a novel robust and real-time long-term tracking framework based on the proposed skimming and perusal modules. The perusal module consists of an effective bounding box regressor to generate a series of candidate proposals and a robust target verifier to infer the optimal candidate with its confidence score. Based on this score, our tracker determines whether the tracked object being present or absent, and then chooses the tracking strategies of local search or global search respectively in the next frame. To speed up the image-wide global search, a novel skimming module is designed to efficiently choose the most possible regions from a large number of sliding windows. Numerous experimental results on the VOT-2018 long-term and OxUvA long-term benchmarks demonstrate that the proposed method achieves the best performance and runs in real-time. The source codes are available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "tracklinic diagnosis of challenge factors in visual tracking"):
  with members:
    - "learning discriminative model prediction for tracking"
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "joint group feature selection and discriminative filter learning for robust visual object tracking"
    - "roi pooled correlation filters for visual tracking"
    - "deeper and wider siamese networks for real time visual 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"

Connected through hyperedge HE-2 (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"
    - "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"
    - "lasot a high quality benchmark for large scale single object tracking"

Connected through hyperedge HE-3 (induced by source paper "gradnet gradient guided network for visual object tracking"):
  with members:
    - "meta learning with latent embedding optimization"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "learning dynamic memory networks for object tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "parallel tracking and verifying a framework for real time and high accuracy visual tracking"
    - "crest convolutional residual learning 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)