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: "characterizing audio adversarial examples using temporal dependency"
  Abstract: Recent studies have highlighted adversarial examples as a ubiquitous threat to different neural network models and many downstream applications. Nonetheless, as unique data properties have inspired distinct and powerful learning principles, this paper aims to explore their potentials towards mitigating adversarial inputs. In particular, our results reveal the importance of using the temporal dependency in audio data to gain discriminate power against adversarial examples. Tested on the automatic speech recognition (ASR) tasks and three recent audio adversarial attacks, we find that (i) input transformation developed from image adversarial defense provides limited robustness improvement and is subtle to advanced attacks; (ii) temporal dependency can be exploited to gain discriminative power against audio adversarial examples and is resistant to adaptive attacks considered in our experiments. Our results not only show promising means of improving the robustness of ASR systems, but also offer novel insights in exploiting domain-specific data properties to mitigate negative effects of adversarial examples.

Connected through hyperedge HE-1 (induced by source paper "anomalous instance detection in deep learning a survey"):
  with members:
    - "unmask adversarial detection and defense through robust feature alignment"
    - "a research agenda dynamic models to defend against correlated attacks"
    - "analysis of confident classifiers for out of distribution detection"
    - "adversarial attacks on time series"
    - "adversarial attacks on deep neural networks for time series classification"
    - "mimicgan robust projection onto image manifolds with corruption mimicking"
    - "analyzing adversarial attacks against deep learning for intrusion detection in iot networks"
    - "deep learning for anomaly detection a survey"

Connected through hyperedge HE-2 (induced by source paper "on adaptive attacks to adversarial example defenses"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "adversarial example detection and classification with asymmetrical adversarial training"
    - "an alternative surrogate loss for pgd based adversarial testing"
    - "mixup inference better exploiting mixup to defend adversarial attacks"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "a new defense against adversarial images turning a weakness into a strength"
    - "resisting adversarial attacks by k winners take all"
    - "rethinking softmax cross entropy loss for adversarial robustness"

Connected through hyperedge HE-3 (induced by source paper "rab provable robustness against backdoor attacks"):
  with members:
    - "dynamic backdoor attacks against machine learning models"
    - "backdoor attacks against transfer learning with pre trained deep learning models"
    - "provable robust learning based on transformation specific smoothing"
    - "on certifying robustness against backdoor attacks via randomized smoothing"
    - "certified robustness to label flipping attacks via randomized smoothing"
    - "certified adversarial robustness via randomized smoothing"
    - "strip a defence against trojan attacks on deep neural networks"
    - "detecting backdoor attacks on deep neural networks by activation clustering"

Connected through hyperedge HE-4 (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"
    - "audio adversarial examples targeted attacks on speech to text"

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"
    - "audio adversarial examples targeted attacks on speech to text"

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"
    - "robust audio adversarial example for a physical attack"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "robust over the air adversarial examples against automatic speech recognition systems"):
  with members:
    - "universal adversarial audio perturbations"
    - "robust audio adversarial example for a physical attack"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "dopa a comprehensive cnn detection methodology against physical adversarial attacks"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "simple physical adversarial examples against end to end autonomous driving models"
    - "adversarial patch"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "a multiversion programming inspired approach to detecting audio adversarial examples"
    - "local gradients smoothing defense against localized adversarial attacks"
    - "robust physical world attacks on deep learning models"
    - "learning transferable architectures for scalable image 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)