human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "inaudible voice commands"
  Abstract: Voice assistants like Siri enable us to control IoT devices conveniently with voice commands, however, they also provide new attack opportunities for adversaries. Previous papers attack voice assistants with obfuscated voice commands by leveraging the gap between speech recognition system and human voice perception. The limitation is that these obfuscated commands are audible and thus conspicuous to device owners. In this paper, we propose a novel mechanism to directly attack the microphone used for sensing voice data with inaudible voice commands. We show that the adversary can exploit the microphone's non-linearity and play well-designed inaudible ultrasounds to cause the microphone to record normal voice commands, and thus control the victim device inconspicuously. We demonstrate via end-to-end real-world experiments that our inaudible voice commands can attack an Android phone and an Amazon Echo device with high success rates at a range of 2-3 meters.

Connected through hyperedge HE-1 (induced by source paper "taxonomy and challenges of out of band signal injection attacks and defenses"):
  with members:
    - "understanding the effectiveness of ultrasonic microphone jammer"
    - "a framework for evaluating security in the presence of signal injection attacks"
    - "trick or heat manipulating critical temperature based control systems using rectification attacks"
    - "spearphone a speech privacy exploit via accelerometer sensed reverberations from smartphone loudspeakers"
    - "oops had the silly thing in reverse optical injection attacks in through led status indicators"
    - "signal jamming attacks against communication based train control attack impact and countermeasure"
    - "systematic classification of side channel attacks a case study for mobile devices"
    - "injected and delivered fabricating implicit control over actuation systems by spoofing inertial sensors"

Connected through hyperedge HE-2 (induced by source paper "adversarial examples for deep learning cyber security analytics"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "certified adversarial robustness via randomized smoothing"
    - "on designing machine learning models for malicious network traffic classification"
    - "rallying adversarial techniques against deep learning for network security"
    - "evading classifiers in discrete domains with provable optimality guarantees"
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deceiving end to end deep learning malware detectors using adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "a robust approach for securing audio classification against adversarial attacks"):
  with members:
    - "bottom up broadcast neural network for music genre classification"
    - "sirenattack generating adversarial audio for end to end acoustic systems"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "adagio interactive experimentation with adversarial attack and defense for audio"
    - "improving transferability of adversarial examples with input diversity"
    - "audio adversarial examples targeted attacks on speech to text"
    - "dropout inference in bayesian neural networks with alpha divergences"

Connected through hyperedge HE-4 (induced by source paper "understanding the effectiveness of ultrasonic microphone jammer"):
  with members:
    - "intelligent virtual assistant knows your life"
    - "dolphinattack inaudible voice commands"

Connected through hyperedge HE-5 (induced by source paper "audio adversarial examples targeted attacks on speech to text"):
  with members:
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial attacks on neural network policies"
    - "dolphinattack inaudible voice commands"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "magnet and efficient defenses against adversarial attacks are not robust to adversarial examples"
    - "vulnerability of deep reinforcement learning to policy induction attacks"
    - "generating adversarial malware examples for black box attacks based on gan"
    - "query efficient black box adversarial examples superceded"

Connected through hyperedge HE-6 (induced by source paper "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"):
  with members:
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "black box adversarial attacks with limited queries and information"
    - "stealing hyperparameters in machine learning"
    - "dolphinattack inaudible voice commands"
    - "english conversational telephone speech recognition by humans and machines"
    - "building competitive direct acoustics to word models for english conversational speech recognition"
    - "a network of deep neural networks for distant speech recognition"
    - "efficient defenses against adversarial attacks"

Connected through hyperedge HE-7 (induced by source paper "adversarial machine learning and speech emotion recognition utilizing generative adversarial networks for robustness"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "phonocardiographic sensing using deep learning for abnormal heartbeat detection"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "transfer learning for improving speech emotion classification accuracy"
    - "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"
    - "audio adversarial examples targeted attacks on speech to text"
    - "variational autoencoders for learning latent representations of speech emotion a preliminary study"
    - "towards robust neural networks via random self ensemble"

Connected through hyperedge HE-8 (induced by source paper "did you hear that adversarial examples against automatic speech recognition"):
  with members:
    - "towards evaluating the robustness of neural networks"
    - "wavenet a generative model for raw audio"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "the limitations of deep learning in adversarial settings"
    - "deepfool a simple and accurate method to fool deep neural networks"

Question: Please tell me which class the center node 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)