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: "isolated and ensemble audio preprocessing methods for detecting adversarial examples against automatic speech recognition"
  Abstract: An adversarial attack is an exploitative process in which minute alterations are made to natural inputs, causing the inputs to be misclassified by neural models. In the field of speech recognition, this has become an issue of increasing significance. Although adversarial attacks were originally introduced in computer vision, they have since infiltrated the realm of speech recognition. In 2017, a genetic attack was shown to be quite potent against the Speech Commands Model. Limited-vocabulary speech classifiers, such as the Speech Commands Model, are used in a variety of applications, particularly in telephony; as such, adversarial examples produced by this attack pose as a major security threat. This paper explores various methods of detecting these adversarial examples with combinations of audio preprocessing. One particular combined defense incorporating compressions, speech coding, filtering, and audio panning was shown to be quite effective against the attack on the Speech Commands Model, detecting audio adversarial examples with 93.5% precision and 91.2% recall.

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"
    - "characterizing audio adversarial examples using temporal dependency"
    - "audio adversarial examples targeted attacks on speech to text"

Connected through hyperedge HE-2 (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"
    - "noise flooding for detecting audio adversarial examples against automatic speech recognition"
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "did you hear that 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-3 (induced by source paper "a multiversion programming inspired approach to detecting audio adversarial examples"):
  with members:
    - "targeted adversarial examples for black box audio systems"
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "a cross architecture instruction embedding model for natural language processing inspired binary code analysis"
    - "neural machine translation inspired binary code similarity comparison beyond function pairs"
    - "audio adversarial examples targeted attacks on speech to text"
    - "generalizable adversarial examples detection based on bi model decision mismatch"
    - "exploiting the inherent limitation of l0 adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "noise flooding for detecting audio adversarial examples against automatic speech recognition"):
  with members:
    - "did you hear that adversarial examples against automatic speech recognition"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "the effects of jpeg and jpeg2000 compression on attacks using adversarial examples"
    - "deflecting adversarial attacks with pixel deflection"
    - "audio adversarial examples targeted attacks on speech to text"
    - "adversarial example defenses ensembles of weak defenses are not strong"
    - "assessing threat of adversarial examples on deep neural networks"
    - "xgboost a scalable tree boosting system"

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)