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 "semanticadv generating adversarial examples via attribute conditional image editing"):
  Abstract: Deep neural networks (DNNs) have achieved great success in various applications due to their strong expressive power. However, recent studies have shown that DNNs are vulnerable to adversarial examples which are manipulated instances targeting to mislead DNNs to make incorrect predictions. Currently, most such adversarial examples try to guarantee "subtle perturbation" by limiting the $L_p$ norm of the perturbation. In this paper, we aim to explore the impact of semantic manipulation on DNNs predictions by manipulating the semantic attributes of images and generate "unrestricted adversarial examples". #R##N#In particular, we propose an algorithm \emph{SemanticAdv} which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets. We conduct extensive experiments to show that the semantic based adversarial examples can not only fool different learning tasks such as face verification and landmark detection, but also achieve high targeted attack success rate against \emph{real-world black-box} services such as Azure face verification service based on transferability. #R##N#To further demonstrate the applicability of \emph{SemanticAdv} beyond face recognition domain, we also generate semantic perturbations on street-view images. Such adversarial examples with controlled semantic manipulation can shed light on further understanding about vulnerabilities of DNNs as well as potential defensive approaches.

Includes members:
  Member "meshadv adversarial meshes for visual recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards stable and efficient training of verifiably robust neural networks")
  Member "semantic adversarial attacks parametric transformations that fool deep classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a differentiable color filter for generating unrestricted adversarial images")
  Member "big but imperceptible adversarial perturbations via semantic manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "natural adversarial examples")
  Member "spatially transformed adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial ranking attack and defense")
  Member "cosface large margin cosine loss for deep face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "accelerated training for massive classification via dynamic class selection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "softmax dissection towards understanding intra and inter class objective for embedding learning")
  Member "large scale gan training for high fidelity natural image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "attacks meet interpretability attribute steered detection of adversarial samples":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "secure and robust machine learning for healthcare a survey")

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.LG(Machine Learning)