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 "improving model drift for robust object tracking"):
  Abstract: Discriminative correlation filters show excellent performance in object tracking. However, in complex scenes, the apparent characteristics of the tracked target are variable, which makes it easy to pollute the model and cause the model drift. In this paper, considering that the secondary peak has a greater impact on the model update, we propose a method for detecting the primary and secondary peaks of the response map. Secondly, a novel confidence function which uses the adaptive update discriminant mechanism is proposed, which yield good robustness. Thirdly, we propose a robust tracker with correlation filters, which uses hand-crafted features and can improve model drift in complex scenes. Finally, in order to cope with the current trackers' multi-feature response merge, we propose a simple exponential adaptive merge approach. Extensive experiments are performed on OTB2013, OTB100 and TC128 datasets. Our approach performs superiorly against several state-of-the-art trackers while runs at speed in real time.

Includes members:
  Member "deeper and wider siamese networks for real time visual tracking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "siamese cascaded region proposal networks for real time visual tracking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "siamese box adaptive network for visual tracking")
  Member "adaptive correlation filters with long term and short term memory for object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a review of visual trackers and analysis of its application to mobile robot")
  Member "distractor aware siamese networks for visual object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "unveiling the power of deep tracking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "siamese box adaptive network for visual tracking")
  Member "fast online object tracking and segmentation a unifying approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "siamrpn evolution of siamese visual tracking with very deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "siamese box adaptive network for visual tracking")
  Member "dcfnet discriminant correlation filters network for visual tracking":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a review of visual trackers and analysis of its application to mobile robot")

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)