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: "automated website fingerprinting through deep learning"
  Abstract: Several studies have shown that the network traffic that is generated by a visit to a website over Tor reveals information specific to the website through the timing and sizes of network packets. By capturing traffic traces between users and their Tor entry guard, a network eavesdropper can leverage this meta-data to reveal which website Tor users are visiting. The success of such attacks heavily depends on the particular set of traffic features that are used to construct the fingerprint. Typically, these features are manually engineered and, as such, any change introduced to the Tor network can render these carefully constructed features ineffective. In this paper, we show that an adversary can automate the feature engineering process, and thus automatically deanonymize Tor traffic by applying our novel method based on deep learning. We collect a dataset comprised of more than three million network traces, which is the largest dataset of web traffic ever used for website fingerprinting, and find that the performance achieved by our deep learning approaches is comparable to known methods which include various research efforts spanning over multiple years. The obtained success rate exceeds 96% for a closed world of 100 websites and 94% for our biggest closed world of 900 classes. In our open world evaluation, the most performant deep learning model is 2% more accurate than the state-of-the-art attack. Furthermore, we show that the implicit features automatically learned by our approach are far more resilient to dynamic changes of web content over time. We conclude that the ability to automatically construct the most relevant traffic features and perform accurate traffic recognition makes our deep learning based approach an efficient, flexible and robust technique for website fingerprinting.

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"
    - "deep fingerprinting undermining website fingerprinting defenses with deep learning"
    - "deepcorr strong flow correlation attacks on tor using 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"
    - "deep fingerprinting undermining website fingerprinting defenses with deep learning"
    - "sok security and privacy in machine 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"
    - "deep fingerprinting undermining website fingerprinting defenses with 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"
    - "deep fingerprinting undermining website fingerprinting defenses with 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 "your smart home can t keep a secret towards automated fingerprinting of iot traffic with neural networks"):
  with members:
    - "deep fingerprinting undermining website fingerprinting defenses with deep learning"
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "peek a boo i see your smart home activities even encrypted"
    - "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-6 (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"
    - "deep fingerprinting undermining website fingerprinting defenses with deep learning"
    - "techniques for interpretable machine learning"
    - "understanding tor usage with privacy preserving measurement"
    - "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-7 (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"
    - "deep fingerprinting undermining website fingerprinting defenses with deep learning"
    - "oblivious dns practical privacy for dns queries"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "evading classifiers in discrete domains with provable optimality guarantees"):
  with members:
    - "pots protective optimization technologies"
    - "scaling provable adversarial defenses"
    - "deep fingerprinting undermining website fingerprinting defenses with deep learning"
    - "cost sensitive robustness against adversarial examples"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deep text classification can be fooled"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"

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)