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 "universal transferable and targeted adversarial attacks"):
  Abstract: Deep Neural Network has been found vulnerable recently. A kind of well-designed inputs, which called adversarial examples, can lead the networks to make incorrect predictions. Depending on the different scenarios, goals and capabilities, the difficulty to generate the attack is different. For example, generating a targeted attack is more difficult than a non-targeted attack, a universal attack is more difficult than a non-universal attack, a transferable attack is more difficult than a nontransferable one. The question is: Is there exist an attack that can survival in the most harsh adversity to meet all these requirements. Although many cheap and effective attacks have been proposed, this question is still not completely solved over large models and large scale dataset. In this paper, we learn a universal mapping from the sources to the adversarial examples. These examples can fool classification networks into classifying all of them to one targeted class. Besides, they are also transferable between different models.

Includes members:
  Member "one pixel attack for fooling deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial ranking attack and defense")
  Member "few shot unsupervised image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stylegan2 distillation for feed forward image manipulation")
  Member "adversarial attacks on neural network policies":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "adversarial examples for semantic segmentation and object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection")
  Member "adversarial examples for evaluating reading comprehension systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "generating natural adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robustness verification for transformers")
  Member "perceptual losses for real time style transfer and super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "transferability in machine learning from phenomena to black box attacks using adversarial samples":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial ranking attack and defense")

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)