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 "semantic adversarial perturbations using learnt representations"):
  Abstract: Adversarial examples for image classifiers are typically created by searching for a suitable norm-constrained perturbation to the pixels of an image. However, such perturbations represent only a small and rather contrived subset of possible adversarial inputs; robustness to norm-constrained pixel perturbations alone is insufficient. We introduce a novel method for the construction of a rich new class of semantic adversarial examples. Leveraging the hierarchical feature representations learnt by generative models, our procedure makes adversarial but realistic changes at different levels of semantic granularity. Unlike prior work, this is not an ad-hoc algorithm targeting a fixed category of semantic property. For instance, our approach perturbs the pose, location, size, shape, colour and texture of the objects in an image without manual encoding of these concepts. We demonstrate this new attack by creating semantic adversarial examples that fool state-of-the-art classifiers on the MNIST and ImageNet datasets.

Includes members:
  Member "semanticadv generating adversarial examples via attribute conditional image editing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a differentiable color filter for generating unrestricted adversarial images")
  Member "algorithms for verifying deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "parot a practical framework for robust deep neural network training")
  Member "generating semantic adversarial examples with differentiable rendering":
    also connected through other hyperedges:
      (none)
  Member "testing robustness against unforeseen adversaries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "understanding the decision boundary of deep neural networks an empirical study")
  Member "benchmarking neural network robustness to common corruptions and perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet")
  Member "adversarial attacks and defenses in images graphs and text a review":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "adaptive generation of unrestricted adversarial inputs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "advspade realistic unrestricted attacks for semantic segmentation")
  Member "semantic adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "breaking certified defenses semantic adversarial examples with spoofed robustness certificates")

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)