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 "robust over the air adversarial examples against automatic speech recognition systems"):
  Abstract: Automatic speech recognition (ASR) systems are possible to fool via targeted adversarial examples. These can induce the ASR to produce arbitrary transcriptions in response to any type of audio signal, be it speech, environmental sounds, or music. However, in general, those adversarial examples did not work in a real-world setup, where the examples are played over the air but have to be fed into the ASR system directly. In some cases, where the adversarial examples could be successfully played over the air, the attacks require precise information about the room where the attack takes place in order to tailor the adversarial examples to a specific setup and are not transferable to other rooms. Other attacks, which are robust in an over-the-air attack, are either handcrafted examples or human listeners can easily recognize the target transcription, once they have been alerted to its content. In this paper, we demonstrate the first generic algorithm that produces adversarial examples which remain robust in an over-the-air attack such that the ASR system transcribes the target transcription after actually being replayed. For the proposed algorithm, guessing a rough approximation of the room characteristics is enough and no actual access to the room is required. We use the ASR system Kaldi to demonstrate the attack and employ a room-impulse-response simulator to harden the adversarial examples against varying room characteristics. Further, the algorithm can also utilize psychoacoustics to hide changes of the original audio signal below the human thresholds of hearing. We show that the adversarial examples work for varying room setups, but also can be tailored to specific room setups. As a result, an attacker can optimize adversarial examples for any target transcription and to arbitrary rooms. Additionally, the adversarial examples remain transferable to varying rooms with a high probability.

Includes members:
  Member "universal adversarial audio perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blind adversarial network perturbations")
  Member "robust audio adversarial example for a physical attack":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "advbox a toolbox to generate adversarial examples that fool neural networks")
  Member "a robust approach for securing audio classification against adversarial attacks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "imperio robust over the air adversarial examples for automatic speech recognition systems")
  Member "rwth asr systems for librispeech hybrid vs attention w o data augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "speech corpus of ainu folklore and end to end speech recognition for ainu language")
  Member "practical hidden voice attacks against speech and speaker recognition systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "security vetting process of smart home assistant applications a first look and case studies")
  Member "on evaluating adversarial robustness":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "noise flooding for detecting audio adversarial examples against automatic speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ensemble noise simulation to handle uncertainty about gradient based adversarial attacks")
  Member "targeted adversarial examples for black box audio systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "g odel s sentence is an adversarial example but unsolvable")

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.CR(Cryptography and Security)