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Center hyperedge (induced by source paper "query efficient black box adversarial examples superceded"):
  Abstract: Note that this paper is superceded by "Black-Box Adversarial Attacks with Limited Queries and Information." #R##N#Current neural network-based image classifiers are susceptible to adversarial examples, even in the black-box setting, where the attacker is limited to query access without access to gradients. Previous methods --- substitute networks and coordinate-based finite-difference methods --- are either unreliable or query-inefficient, making these methods impractical for certain problems. #R##N#We introduce a new method for reliably generating adversarial examples under more restricted, practical black-box threat models. First, we apply natural evolution strategies to perform black-box attacks using two to three orders of magnitude fewer queries than previous methods. Second, we introduce a new algorithm to perform targeted adversarial attacks in the partial-information setting, where the attacker only has access to a limited number of target classes. Using these techniques, we successfully perform the first targeted adversarial attack against a commercially deployed machine learning system, the Google Cloud Vision API, in the partial information setting.

Includes members:
  Member "synthesizing robust adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial ranking attack and defense")
  Member "practical black box attacks against machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial ranking attack and defense")
  Member "transferability in machine learning from phenomena to black box attacks using adversarial samples":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial ranking attack and defense")
  Member "delving into transferable adversarial examples and black box attacks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial ranking attack and defense")
  Member "approximation with random bases":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep randomized neural networks")
  Member "towards evaluating the robustness of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial ranking attack and defense")
  Member "adversarial examples in the physical world":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial ranking attack and defense")
  Member "rethinking the inception architecture for computer vision":
    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.CV(Computer Vision and Pattern Recognition)