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 "making targeted black box evasion attacks effective and efficient"):
  Abstract: We investigate how an adversary can optimally use its query budget for targeted evasion attacks against deep neural networks in a black-box setting. We formalize the problem setting and systematically evaluate what benefits the adversary can gain by using substitute models. We show that there is an exploration-exploitation tradeoff in that query efficiency comes at the cost of effectiveness. We present two new attack strategies for using substitute models and show that they are as effective as previous query-only techniques but require significantly fewer queries, by up to three orders of magnitude. We also show that an agile adversary capable of switching through different attack techniques can achieve pareto-optimal efficiency. We demonstrate our attack against Google Cloud Vision showing that the difficulty of black-box attacks against real-world prediction APIs is significantly easier than previously thought (requiring approximately 500 queries instead of approximately 20,000 as in previous works).

Includes members:
  Member "copy and paste a simple but effective initialization method for black box adversarial attacks":
    also connected through other hyperedges:
      (none)
  Member "reducing adversarial example transferability using gradient regularization":
    also connected through other hyperedges:
      (none)
  Member "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey of black box adversarial attacks on computer vision models")
  Member "model reuse attacks on deep learning systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sentinet detecting localized universal attacks against deep learning systems")
  Member "on the suitability of l_p norms for creating and preventing adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a differentiable color filter for generating unrestricted adversarial images")
  Member "query efficient black box attack by active learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stealing black box functionality using the deep neural tree architecture")
  Member "prada protecting against dnn model stealing attacks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "nass optimizing secure inference via neural architecture search")
  Member "genattack practical black box attacks with gradient free optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "one man s trash is another man s treasure resisting adversarial examples by adversarial examples")

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)