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 "motion excited sampler video adversarial attack with sparked prior"):
  Abstract: Deep neural networks are known to be susceptible to adversarial noise, which are tiny and imperceptible perturbations. Most of previous work on adversarial attack mainly focus on image models, while the vulnerability of video models is less explored. In this paper, we aim to attack video models by utilizing intrinsic movement pattern and regional relative motion among video frames. We propose an effective motion-excited sampler to obtain motion-aware noise prior, which we term as sparked prior. Our sparked prior underlines frame correlations and utilizes video dynamics via relative motion. By using the sparked prior in gradient estimation, we can successfully attack a variety of video classification models with fewer number of queries. Extensive experimental results on four benchmark datasets validate the efficacy of our proposed method.

Includes members:
  Member "sparse black box video attack with reinforcement learning":
    also connected through other hyperedges:
      (none)
  Member "heuristic black box adversarial attacks on video recognition models":
    also connected through other hyperedges:
      (none)
  Member "deep video inpainting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient semantic video segmentation with per frame inference")
  Member "appending adversarial frames for universal video attack":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "temporal sparse adversarial attack on gait recognition")
  Member "sequence level semantics aggregation for video object detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "plug play convolutional regression tracker for video object detection")
  Member "black box adversarial attacks on video recognition models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "skip connections matter on the transferability of adversarial examples generated with resnets")
  Member "adversarial attacks for optical flow based action recognition classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "smart skeletal motion action recognition attack")
  Member "sparse adversarial perturbations for videos":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "patternless adversarial attacks on video recognition networks")

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)