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: "noise flooding for detecting audio adversarial examples against automatic speech recognition"
  Abstract: Neural models enjoy widespread use across a variety of tasks and have grown to become crucial components of many industrial systems. Despite their effectiveness and extensive popularity, they are not without their exploitable flaws. Initially applied to computer vision systems, the generation of adversarial examples is a process in which seemingly imperceptible perturbations are made to an image, with the purpose of inducing a deep learning based classifier to misclassify the image. Due to recent trends in speech processing, this has become a noticeable issue in speech recognition models. In late 2017, an attack was shown to be quite effective against the Speech Commands classification model. Limited-vocabulary speech classifiers, such as the Speech Commands model, are used quite frequently in a variety of applications, particularly in managing automated attendants in telephony contexts. As such, adversarial examples produced by this attack could have real-world consequences. While previous work in defending against these adversarial examples has investigated using audio preprocessing to reduce or distort adversarial noise, this work explores the idea of flooding p articular frequency bands of an audio signal with random noise in order to detect adversarial examples. This technique of flooding, which does not require retraining or modifying the model, is inspired by work done in computer vision and builds on the idea that speech classifiers are relatively robust to natural noise. A combined defense incorporating 5 different frequency bands for flooding the signal with noise outperformed other existing defenses in the audio space, detecting adversarial examples with 91.8% precision and 93.5% recall.

Connected through hyperedge HE-1 (induced by source paper "ensemble noise simulation to handle uncertainty about gradient based adversarial attacks"):
  with members:
    - "transferable adversarial robustness using adversarially trained autoencoders"
    - "a computationally efficient method for defending adversarial deep learning attacks"
    - "training multi task adversarial network for extracting noise robust speaker embedding"
    - "breaking transferability of adversarial samples with randomness"
    - "boosting noise robustness of acoustic model via deep adversarial training"
    - "combatting adversarial attacks through denoising and dimensionality reduction a cascaded autoencoder approach"
    - "enhancing robustness of machine learning systems via data transformations"
    - "magnet a two pronged defense against adversarial examples"

Connected through hyperedge HE-2 (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"
    - "targeted adversarial examples for black box audio systems"

Connected through hyperedge HE-3 (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"
    - "targeted adversarial examples for black box audio systems"
    - "commandersong a systematic approach for practical adversarial voice recognition"

Connected through hyperedge HE-4 (induced by source paper "adversarial example detection by classification for deep speech recognition"):
  with members:
    - "adversarial learning in statistical classification a comprehensive review of defenses against attacks"
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "isolated and ensemble audio preprocessing methods for detecting adversarial examples against automatic speech recognition"
    - "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"
    - "characterizing audio adversarial examples using temporal dependency"
    - "audio adversarial examples targeted attacks on speech to text"
    - "feature squeezing detecting adversarial examples in deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "lance a comprehensive and lightweight cnn defense methodology against physical adversarial attacks on embedded multimedia applications"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "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"
    - "characterizing audio adversarial examples using temporal dependency"
    - "robust physical world attacks on deep learning models"

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.SD(Sound)