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: "using non invertible data transformations to build adversarial robust neural networks"
  Abstract: Deep neural networks have proven to be quite effective in a wide variety of machine learning tasks, ranging from improved speech recognition systems to advancing the development of autonomous vehicles. However, despite their superior performance in many applications, these models have been recently shown to be susceptible to a particular type of attack possible through the generation of particular synthetic examples referred to as adversarial samples. These samples are constructed by manipulating real examples from the training data distribution in order to "fool" the original neural model, resulting in misclassification (with high confidence) of previously correctly classified samples. Addressing this weakness is of utmost importance if deep neural architectures are to be applied to critical applications, such as those in the domain of cybersecurity. In this paper, we present an analysis of this fundamental flaw lurking in all neural architectures to uncover limitations of previously proposed defense mechanisms. More importantly, we present a unifying framework for protecting deep neural models using a non-invertible data transformation--developing two adversary-resilient architectures utilizing both linear and nonlinear dimensionality reduction. Empirical results indicate that our framework provides better robustness compared to state-of-art solutions while having negligible degradation in accuracy.

Connected through hyperedge HE-1 (induced by source paper "characterizing audio adversarial examples using temporal dependency"):
  with members:
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"
    - "audio adversarial examples targeted attacks on speech to text"
    - "on the limitation of magnet defense against l_1 based adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "blocking transferability of adversarial examples in black box learning systems"):
  with members:
    - "sok security and privacy in machine learning"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "assessing threat of adversarial examples on deep neural networks"
    - "suppressing the unusual towards robust cnns using symmetric activation functions"
    - "practical black box attacks against machine learning"
    - "towards robust deep neural networks with bang"
    - "adversary resistant deep neural networks with an application to malware detection"

Connected through hyperedge HE-3 (induced by source paper "dna steganalysis using deep recurrent neural networks"):
  with members:
    - "on the statistical detection of adversarial examples"
    - "deepmirgene deep neural network based precursor microrna prediction"
    - "deep learning in bioinformatics"
    - "learning the number of neurons in deep networks"
    - "dna level splice junction prediction using deep recurrent neural networks"
    - "unsupervised learning of video representations using lstms"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "draw a recurrent neural network for image generation"

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)