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 "abba saliency regularized motion based adversarial blur attack"):
  Abstract: Deep neural networks are vulnerable to noise-based adversarial examples, which can mislead the networks by adding random-like noise. However, such examples are hardly found in the real world and easily perceived when thumping noises are used to keep their high transferability across different models. In this paper, we identify a new attacking method termed motion-based adversarial blur attack (ABBA) that can generate visually natural motion-blurred adversarial examples even with relatively high perturbation, allowing much better transferability than noise-based methods. To this end, we first formulate the kernel-prediction-based attack where an input image is convolved with kernels in a pixel-wise way, and the misclassification capability is achieved by tuning the kernel weights. To generate visually more natural and plausible examples, we further propose the saliency-regularized adversarial kernel prediction where the salient region serves as a moving object, and the predicted kernel is regularized to achieve naturally visual effects. Besides, the attack can be further enhanced by adaptively tuning the translations of object and background. Extensive experimental results on the NeurIPS'17 adversarial competition dataset validate the effectiveness of ABBA by considering various kernel sizes, translations, and regions. Furthermore, we study the effects of state-of-the-art GAN-based deblurring mechanisms to our methods.

Includes members:
  Member "effects of blur and deblurring to visual object tracking":
    also connected through other hyperedges:
      (none)
  Member "brain inspired reverse adversarial examples":
    also connected through other hyperedges:
      (none)
  Member "spatial aware online adversarial perturbations against visual object tracking":
    also connected through other hyperedges:
      (none)
  Member "a simple pooling based design for real time salient object detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "evading defenses to transferable adversarial examples by translation invariant attacks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial ranking attack and defense")
  Member "amora black box adversarial morphing attack":
    also connected through other hyperedges:
      (none)
  Member "adversarial attacks and defences competition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nesterov accelerated gradient and scale invariance for adversarial attacks")
  Member "learning to synthesize motion blur":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "softmax splatting for video frame interpolation")

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)