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: "did you hear that adversarial examples against automatic speech recognition"
  Abstract: Speech is a common and effective way of communication between humans, and modern consumer devices such as smartphones and home hubs are equipped with deep learning based accurate automatic speech recognition to enable natural interaction between humans and machines. Recently, researchers have demonstrated powerful attacks against machine learning models that can fool them to produceincorrect results. However, nearly all previous research in adversarial attacks has focused on image recognition and object detection models. In this short paper, we present a first of its kind demonstration of adversarial attacks against speech classification model. Our algorithm performs targeted attacks with 87% success by adding small background noise without having to know the underlying model parameter and architecture. Our attack only changes the least significant bits of a subset of audio clip samples, and the noise does not change 89% the human listener's perception of the audio clip as evaluated in our human study.

Connected through hyperedge HE-1 (induced by source paper "towards query efficient black box adversary with zeroth order natural gradient descent"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "on the design of black box adversarial examples by leveraging gradient free optimization and operator splitting method"
    - "fault sneaking attack a stealthy framework for misleading deep neural networks"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "hessian aware zeroth order optimization for black box adversarial attack"
    - "defensive dropout for hardening deep neural networks under adversarial attacks"
    - "black box adversarial attacks with limited queries and information"
    - "structured adversarial attack towards general implementation and better interpretability"

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "one pixel attack for fooling deep neural networks"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "self organizing classifiers first steps in structured evolutionary machine learning"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"
    - "self organizing classifiers and niched fitness"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "classification regions of deep neural networks"
    - "exploring the landscape of spatial robustness"

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 "random directional attack for fooling deep neural networks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "semantic adversarial examples"
    - "disentangling adversarial robustness and generalization"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "comdefend an efficient image compression model to defend adversarial examples"
    - "divide denoise and defend against adversarial attacks"

Connected through hyperedge HE-6 (induced by source paper "adversarial examples for models of code"):
  with members:
    - "combating adversarial misspellings with robust word recognition"
    - "gnn film graph neural networks with feature wise linear modulation"
    - "when deep learning met code search"
    - "getafix learning to fix bugs automatically"
    - "structural language models for any code generation"
    - "code2vec learning distributed representations of code"
    - "targeted adversarial examples for black box audio systems"
    - "a general path based representation for predicting program properties"

Connected through hyperedge HE-7 (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"
    - "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"
    - "characterizing audio adversarial examples using temporal dependency"
    - "audio adversarial examples targeted attacks on speech to text"

Connected through hyperedge HE-8 (induced by source paper "upc learning universal physical camouflage attacks on object detectors"):
  with members:
    - "humans can decipher adversarial images"
    - "robust adversarial perturbation on deep proposal based models"
    - "textbugger generating adversarial text against real world applications"
    - "yolov3 an incremental improvement"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "dpatch an adversarial patch attack on object detectors"
    - "black box adversarial attacks with limited queries and information"
    - "audio adversarial examples targeted attacks on speech to text"

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.CL(Computation and Language)