human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "tracklinic diagnosis of challenge factors in visual tracking"):
  Abstract: Generic visual tracking is difficult due to many challenge factors (e.g., occlusion, blur, etc.). Each of these factors may cause serious problems for a tracking algorithm, and when they work together can make things even more complicated. Despite a great amount of efforts devoted to understanding the behavior of tracking algorithms, reliable and quantifiable ways for studying the per factor tracking behavior remain barely available. Addressing this issue, in this paper we contribute to the community a tracking diagnosis toolkit, TracKlinic, for diagnosis of challenge factors of tracking algorithms. #R##N#TracKlinic consists of two novel components focusing on the data and analysis aspects, respectively. For the data component, we carefully prepare a set of 2,390 annotated videos, each involving one and only one major challenge factor. When analyzing an algorithm for a specific challenge factor, such one-factor-per-sequence rule greatly inhibits the disturbance from other factors and consequently leads to more faithful analysis. For the analysis component, given the tracking results on all sequences, it investigates the behavior of the tracker under each individual factor and generates the report automatically. With TracKlinic, a thorough study is conducted on ten state-of-the-art trackers on nine challenge factors (including two compound ones). The results suggest that, heavy shape variation and occlusion are the two most challenging factors faced by most trackers. Besides, out-of-view, though does not happen frequently, is often fatal. By sharing TracKlinic, we expect to make it much easier for diagnosing tracking algorithms, and to thus facilitate developing better ones.

Includes members:
  Member "learning discriminative model prediction for tracking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "siamese box adaptive network for visual tracking")
  Member "got 10k a large high diversity benchmark for generic object tracking in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "siamese box adaptive network for visual tracking")
  Member "joint group feature selection and discriminative filter learning for robust visual object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi drone based single object tracking with agent sharing network")
  Member "roi pooled correlation filters for visual tracking":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spark spatial aware online incremental attack against visual tracking")
  Member "skimming perusal tracking a framework for real time and robust long term tracking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "globaltrack a simple and strong baseline for long term tracking")
  Member "deeper and wider siamese networks for real time visual tracking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "atom accurate tracking by overlap maximization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "siamese box adaptive network for visual tracking")
  Member "long term tracking in the wild a benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mast a memory augmented self supervised tracker")

Question: Please tell me which class the source paper 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)