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 "walking on the edge fast low distortion adversarial examples"):
  Abstract: Adversarial examples of deep neural networks are receiving ever increasing attention because they help in understanding and reducing the sensitivity to their input. This is natural given the increasing applications of deep neural networks in our everyday lives. When white-box attacks are almost always successful, it is typically only the distortion of the perturbations that matters in their evaluation. In this work, we argue that speed is important as well, especially when considering that fast attacks are required by adversarial training. Given more time, iterative methods can always find better solutions. We investigate this speed-distortion trade-off in some depth and introduce a new attack called boundary projection (BP) that improves upon existing methods by a large margin. Our key idea is that the classification boundary is a manifold in the image space: we therefore quickly reach the boundary and then optimize distortion on this manifold.

Includes members:
  Member "targeted mismatch adversarial attack query with a flower to retrieve the tower":
    also connected through other hyperedges:
      (none)
  Member "adversarial attacks and defences competition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nesterov accelerated gradient and scale invariance for adversarial attacks")
  Member "on the suitability of l_p norms for creating and preventing adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a differentiable color filter for generating unrestricted adversarial images")
  Member "universal perturbation attack against image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial ranking attack and defense")
  Member "decoupling direction and norm for efficient gradient based l2 adversarial attacks and defenses":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "increasing the robustness of dnns against image corruptions by playing the game of noise")
  Member "audio adversarial examples targeted attacks on speech to text":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "advbox a toolbox to generate adversarial examples that fool neural networks")
  Member "why do deep convolutional networks generalize so poorly to small image transformations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation")
  Member "adversarial examples for semantic segmentation and object detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection")

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)