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 "things you may not know about adversarial example a black box adversarial image attack"):
  Abstract: Numerous methods for crafting adversarial examples were proposed recently with high attack success rate. Most of the existing works normalize images into a continuous vector, domain firstly, and then craft adversarial examples in the continuous vector space. However, "adversarial" examples may become benign after de-normalizing them back into discrete integer domain, known as the discretization problem. The discretization problem was mentioned in some work, but was despised and have received relatively little attention. #R##N#In this work, we conduct the first comprehensive study of this discretization problem. We theoretically analyzed 34 representative methods and empirically studied 20 representative open source tools for crafting discretization images. Our findings reveal that almost all of existing works suffer from the discretization problem and the problem is far more serious than we thought. This suggests that the discretization problem should be taken into account when crafting adversarial examples. As a first step towards addressing the discretization problem, we propose a black-box attack method to encode the adversarial example searching problem as a derivative-free optimization problem. Our method is able to craft "real'' adversarial images by derivative-free search on the discrete integer domain. Experimental results show that our method achieves significantly higher attack success rates on the discrete integer domain than most of the other tools, no matter white-box or black-box. Moreover, our method is able to handle any model that is not differentiable and we successfully break the winner of NIPS 17 competition on defense with a 95\% success rate.

Includes members:
  Member "spatially transformed adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial ranking attack and defense")
  Member "formal security analysis of neural networks using symbolic intervals":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robustness verification for transformers")
  Member "deepgauge multi granularity testing criteria for deep learning systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "importance driven deep learning system testing")
  Member "concolic testing for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "importance driven deep learning system testing")
  Member "reachability analysis of deep neural networks with provable guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "metrics and methods for robustness evaluation of neural networks with generative models")
  Member "testing deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "importance driven deep learning system testing")
  Member "evaluating robustness of neural networks with mixed integer programming":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lossless compression of deep neural networks")
  Member "adversarial examples are not easily detected bypassing ten detection methods":
    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)