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 "continuity stability and integration novel tracking based perspectives for temporal object detection"):
  Abstract: Video object detection (VID) has been vigorously studied for years but almost all literature adopts a static accuracy-based evaluation, i.e., mean average precision (mAP). From a temporal perspective, the importance of recall continuity and localization stability is equal to that of accuracy, but the mAP is insufficient to reflect detectors' performance across time. In this paper, non-reference assessments are proposed for continuity and stability based on tubelets from multi-object tracking (MOT). These temporal evaluations can serve as supplements to static mAP. Further, we develop tubelet refinement for improving detectors' performance on temporal continuity and stability through short tubelet suppression, fragment filling, and history-present fusion. #R##N#In addition, we propose a small-overlap suppression to extend VID methods to single object tracking (SOT) task. The VID-based SOT does not need MOT or traditional SOT model. A unified VID-MOT-SOT framework is then formed. #R##N#Extensive experiments are conducted on ImageNet VID dataset, where the superiority of our proposed approaches are validated and verified. Codes will be publicly available.

Includes members:
  Member "siamfc towards robust and accurate visual tracking with target estimation guidelines":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "state aware tracker for real time video object segmentation")
  Member "atom accurate tracking by overlap maximization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "siamese box adaptive network for visual tracking")
  Member "object detection in video with spatiotemporal sampling networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint anchor feature refinement for real time accurate object detection in images and videos")
  Member "detect or track towards cost effective video object detection tracking":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tracking of micro unmanned aerial vehicles a comparative study")
  Member "fast online object tracking and segmentation a unifying approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "t cnn tubelets with convolutional neural networks for object detection from videos":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "animal detection in man made environments")
  Member "towards high performance video object detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "joint anchor feature refinement for real time accurate object detection in images and videos")
  Member "focal loss for dense object detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")

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)