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 "detect or track towards cost effective video object detection tracking"):
  Abstract: State-of-the-art object detectors and trackers are developing fast. Trackers are in general more efficient than detectors but bear the risk of drifting. A question is hence raised -- how to improve the accuracy of video object detection/tracking by utilizing the existing detectors and trackers within a given time budget? A baseline is frame skipping -- detecting every N-th frames and tracking for the frames in between. This baseline, however, is suboptimal since the detection frequency should depend on the tracking quality. To this end, we propose a scheduler network, which determines to detect or track at a certain frame, as a generalization of Siamese trackers. Although being light-weight and simple in structure, the scheduler network is more effective than the frame skipping baselines and flow-based approaches, as validated on ImageNet VID dataset in video object detection/tracking.

Includes members:
  Member "a twofold siamese network for real time object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spark spatial aware online incremental attack against visual tracking")
  Member "dynamic video segmentation network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient semantic video segmentation with per frame inference")
  Member "optimizing video object detection via a scale time lattice":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "plug play convolutional regression tracker for video object detection")
  Member "low latency video semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dual temporal memory network for efficient video object segmentation")
  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 "object detection in videos by high quality object linking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "proposal learning for semi supervised object detection")
  Member "pcl proposal cluster learning for weakly supervised object detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "starnet towards weakly supervised few shot detection and explainable few shot classification")
  Member "towards high performance video object detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "joint anchor feature refinement for real time accurate object detection in images and videos")

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)