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: "robust audio adversarial example for a physical attack"
  Abstract: We propose a method to generate audio adversarial examples that can attack a state-of-the-art speech recognition model in the physical world. Previous work assumes that generated adversarial examples are directly fed to the recognition model, and is not able to perform such a physical attack because of reverberation and noise from playback environments. In contrast, our method obtains robust adversarial examples by simulating transformations caused by playback or recording in the physical world and incorporating the transformations into the generation process. Evaluation and a listening experiment demonstrated that our adversarial examples are able to attack without being noticed by humans. This result suggests that audio adversarial examples generated by the proposed method may become a real threat.

Connected through hyperedge HE-1 (induced by source paper "advbox a toolbox to generate adversarial examples that fool neural networks"):
  with members:
    - "fastwordbug a fast method to generate adversarial text against nlp applications"
    - "transferability of adversarial examples to attack cloud based image classifier service"
    - "daedalus breaking non maximum suppression in object detection via adversarial examples"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "a tour of convolutional networks guided by linear interpreters"
    - "adversarial examples versus cloud based detectors a black box empirical study"
    - "adversarial t shirt evading person detectors in a physical world"
    - "textbugger generating adversarial text against real world applications"

Connected through hyperedge HE-2 (induced by source paper "indirect adversarial attacks via poisoning neighbors for graph convolutional networks"):
  with members:
    - "adversarial attacks on graph neural networks via meta learning"
    - "adversarial attack on graph structured data"
    - "graph convolutional neural networks for web scale recommender systems"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "robust physical world attacks on deep learning models"
    - "toeplitz inverse covariance based clustering of multivariate time series data"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-3 (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 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"
    - "audio adversarial examples targeted attacks on speech to text"

Connected through hyperedge HE-4 (induced by source paper "adversarial examples for deep learning cyber security analytics"):
  with members:
    - "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"
    - "learning to evade static pe machine learning malware models via reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "universal adversarial audio perturbations"):
  with members:
    - "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"
    - "knockoff nets stealing functionality of black box models"

Connected through hyperedge HE-6 (induced by source paper "imperio robust over the air adversarial examples for automatic speech recognition systems"):
  with members:
    - "universal adversarial audio perturbations"
    - "a robust approach for securing audio classification against adversarial attacks"
    - "universal adversarial perturbations for speech recognition systems"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "practical hidden voice attacks against speech and speaker recognition systems"
    - "on evaluating adversarial robustness"
    - "noise flooding for detecting audio adversarial examples against automatic speech recognition"
    - "targeted adversarial examples for black box audio systems"

Connected through hyperedge HE-7 (induced by source paper "adversarial attacks on copyright detection systems"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "daedalus breaking non maximum suppression in object detection via adversarial examples"
    - "adversarial training for free"
    - "targeted adversarial examples for black box audio systems"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "universal adversarial training"
    - "audio adversarial examples targeted attacks on speech to text"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-8 (induced by source paper "robust over the air adversarial examples against automatic speech recognition systems"):
  with members:
    - "universal adversarial audio perturbations"
    - "a robust approach for securing audio classification against adversarial attacks"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "practical hidden voice attacks against speech and speaker recognition systems"
    - "on evaluating adversarial robustness"
    - "noise flooding for detecting audio adversarial examples against automatic speech recognition"
    - "targeted adversarial examples for black box audio systems"
    - "commandersong a systematic approach for practical adversarial voice recognition"

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)