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: "deep fingerprinting undermining website fingerprinting defenses with deep learning"
  Abstract: Website fingerprinting enables a local eavesdropper to determine which websites a user is visiting over an encrypted connection, and the state-of-the-art attacks have been shown to be effective even when the user is protecting her privacy with the Tor anonymity system. Recently, lightweight website fingerprinting defenses for Tor have emerged that substantially degrade existing attacks, including WTF-PAD and Walkie-Talkie. In this work, we propose a new website fingerprinting attack against Tor that leverages a type of deep learning called convolution neural networks (CNN), and we evaluate this attack against WTF-PAD and Walkie-Talkie. The CNN attack attains over 98% accuracy on Tor traffic without defenses, better than all prior attacks, and it is also effective against WTF-PAD with over 90% accuracy. Walkie-Talkie remains effective, holding the attack to just 53% accuracy. In a realistic open-world setting, the CNN attack remains effective, with a 93% true positive rate and a less than 1% false positive rate on undefended traffic. Against traffic defended with WTF-PAD in this setting, the attack still can get a 60% true positive rate with a less than 1% false positive rate. These findings indicate a need for more study of robust defenses that could be deployed in Tor.

Connected through hyperedge HE-1 (induced by source paper "blind adversarial network perturbations"):
  with members:
    - "universal adversarial audio perturbations"
    - "one pixel attack for fooling deep neural networks"
    - "mockingbird defending against deep learning based website fingerprinting attacks with adversarial traces"
    - "deepcorr strong flow correlation attacks on tor using deep learning"
    - "automated website fingerprinting through deep learning"
    - "var cnn and dynaflow improved attacks and defenses for website fingerprinting"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "learning universal adversarial perturbations with generative models"

Connected through hyperedge HE-2 (induced by source paper "adversarial network traffic toward evaluating the robustness of deep learning based network traffic classification"):
  with members:
    - "how to achieve high classification accuracy with just a few labels a semi supervised approach using sampled packets"
    - "sok security and privacy in machine learning"
    - "automated website fingerprinting through deep learning"
    - "robust smartphone app identification via encrypted network traffic analysis"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "universal adversarial perturbations"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"

Connected through hyperedge HE-3 (induced by source paper "mockingbird defending against deep learning based website fingerprinting attacks with adversarial traces"):
  with members:
    - "var cnn a data efficient website fingerprinting attack based on deep learning"
    - "measuring information leakage in website fingerprinting attacks and defenses"
    - "tik tok the utility of packet timing in website fingerprinting attacks"
    - "automated website fingerprinting through deep learning"
    - "certified defenses against adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "magnet and efficient defenses against adversarial attacks are not robust to adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "adv dwf defending against deep learning based website fingerprinting attacks with adversarial traces"):
  with members:
    - "measuring information leakage in website fingerprinting attacks and defenses"
    - "automated website fingerprinting through deep learning"
    - "certified defenses against adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "magnet and efficient defenses against adversarial attacks are not robust to adversarial examples"
    - "bayes not naive security bounds on website fingerprinting defenses"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-5 (induced by source paper "var cnn a data efficient website fingerprinting attack based on deep learning"):
  with members:
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "understanding deep learning requires rethinking generalization"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"
    - "wavenet a generative model for raw audio"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-6 (induced by source paper "your smart home can t keep a secret towards automated fingerprinting of iot traffic with neural networks"):
  with members:
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "peek a boo i see your smart home activities even encrypted"
    - "automated website fingerprinting through deep learning"
    - "a smart home is no castle privacy vulnerabilities of encrypted iot traffic"
    - "spying on the smart home privacy attacks and defenses on encrypted iot traffic"
    - "iotscanner detecting and classifying privacy threats in iot neighborhoods"
    - "iot sentinel demo automated device type identification for security enforcement in iot"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-7 (induced by source paper "tik tok the utility of packet timing in website fingerprinting attacks"):
  with members:
    - "var cnn a data efficient website fingerprinting attack based on deep learning"
    - "measuring information leakage in website fingerprinting attacks and defenses"
    - "techniques for interpretable machine learning"
    - "understanding tor usage with privacy preserving measurement"
    - "automated website fingerprinting through deep learning"
    - "how unique is your onion an analysis of the fingerprintability of tor onion services"
    - "k fingerprinting a robust scalable website fingerprinting technique"
    - "toward an efficient website fingerprinting defense"

Connected through hyperedge HE-8 (induced by source paper "encrypted dns privacy a traffic analysis perspective"):
  with members:
    - "analyzing the costs and benefits of dns dot and doh for the modern web"
    - "oblivious dns practical privacy for dns queries"
    - "automated website fingerprinting through deep learning"
    - "how unique is your onion an analysis of the fingerprintability of tor onion services"
    - "k fingerprinting a robust scalable website fingerprinting technique"
    - "toward an efficient website fingerprinting defense"
    - "i know why you went to the clinic risks and realization of https traffic analysis"

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.CR(Cryptography and Security)