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: "crafting adversarial examples for speech paralinguistics applications"
  Abstract: Computational paralinguistic analysis is increasingly being used in a wide range of applications, including security-sensitive applications such as speaker verification, deceptive speech detection, and medical diagnosis. While state-of-the-art machine learning techniques, such as deep neural networks, can provide robust and accurate speech analysis, they are susceptible to adversarial attacks. In this work, we propose a novel end-to-end scheme to generate adversarial examples by perturbing directly the raw waveform of an audio recording rather than specific acoustic features. Our experiments show that the proposed adversarial perturbation can lead to a significant performance drop of state-of-the-art deep neural networks, while only minimally impairing the audio quality.

Connected through hyperedge HE-1 (induced by source paper "identifying audio adversarial examples via anomalous pattern detection"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "robust audio adversarial example for a physical attack"
    - "robust over the air adversarial examples against automatic speech recognition systems"
    - "targeted adversarial examples for black box audio systems"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "adagio interactive experimentation with adversarial attack and defense for audio"
    - "isolated and ensemble audio preprocessing methods for detecting adversarial examples against automatic speech recognition"
    - "characterizing audio adversarial examples using temporal dependency"

Connected through hyperedge HE-2 (induced by source paper "detecting replay attacks using multi channel audio a neural network based method"):
  with members:
    - "remasc realistic replay attack corpus for voice controlled systems"
    - "real time adversarial attacks"
    - "protecting voice controlled systems using sound source identification based on acoustic cues"
    - "an overview of vulnerabilities of voice controlled systems"
    - "the insecurity of home digital voice assistants amazon alexa as a case study"
    - "a study on replay attack and anti spoofing for automatic speaker verification"
    - "decoupled weight decay regularization"
    - "your voice assistant is mine how to abuse speakers to steal information and control your phone"

Connected through hyperedge HE-3 (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-4 (induced by source paper "universal adversarial audio perturbations"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "end to end environmental sound classification using a 1d convolutional neural network"
    - "universal adversarial perturbations for speech recognition systems"
    - "sirenattack generating adversarial audio for end to end acoustic systems"
    - "adversarial initialization when your network performs the way i want"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "unsupervised text style transfer using language models as discriminators"

Connected through hyperedge HE-5 (induced by source paper "hear no evil see kenansville efficient and transferable black box attacks on speech recognition and voice identification systems"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "practical hidden voice attacks against speech and speaker recognition systems"
    - "targeted adversarial examples for black box audio systems"
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "adversarial patch"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"
    - "characterizing audio adversarial examples using temporal dependency"

Connected through hyperedge HE-6 (induced by source paper "test metrics for recurrent neural networks"):
  with members:
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "recurrent neural networks for time series forecasting"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "on adversarial examples for character level neural machine translation"
    - "deepcruiser automated guided testing for stateful deep learning systems"
    - "symbolic execution for deep neural networks"
    - "towards dependability metrics for neural networks"
    - "deepmutation mutation testing of deep learning systems"

Connected through hyperedge HE-7 (induced by source paper "sirenattack generating adversarial audio for end to end acoustic systems"):
  with members:
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "audio adversarial examples targeted attacks on speech to text"
    - "continuous authentication for voice assistants"
    - "dolphinattack inaudible voice commands"
    - "the insecurity of home digital voice assistants amazon alexa as a case study"
    - "dual path networks"
    - "exploring neural transducers for end to end speech recognition"
    - "knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes"

Connected through hyperedge HE-8 (induced by source paper "smart home personal assistants a security and privacy review"):
  with members:
    - "intelligent virtual assistant knows your life"
    - "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"
    - "contextual slot carryover for disparate schemas"
    - "user perceptions of smart home iot privacy"
    - "understanding and mitigating the security risks of voice controlled third party skills on amazon alexa and google home"
    - "sok security and privacy in machine learning"
    - "audio adversarial examples targeted attacks on speech to text"
    - "comparing speech and keyboard text entry for short messages in two languages on touchscreen phones"

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.LG(Machine Learning)