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: "k fingerprinting a robust scalable website fingerprinting technique"
  Abstract: Website fingerprinting enables an attacker to infer which web page a client is browsing through encrypted or anonymized network connections. We present a new website fingerprinting technique based on random decision forests and evaluate performance over standard web pages as well as Tor hidden services, on a larger scale than previous works. Our technique, k-fingerprinting, performs better than current state-of-the-art attacks even against website fingerprinting defenses, and we show that it is possible to launch a website fingerprinting attack in the face of a large amount of noisy data. We can correctly determine which of 30 monitored hidden services a client is visiting with 85% true positive rate (TPR), a false positive rate (FPR) as low as 0.02%, from a world size of 100,000 unmonitored web pages. We further show that error rates vary widely between web resources, and thus some patterns of use will be predictably more vulnerable to attack than others.

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"
    - "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"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "var cnn a data efficient website fingerprinting attack based on deep learning"):
  with members:
    - "deep fingerprinting undermining website fingerprinting defenses with deep learning"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "assessing the privacy benefits of domain name encryption"):
  with members:
    - "analyzing the costs and benefits of dns dot and doh for the modern web"
    - "iclab a global longitudinal internet censorship measurement platform"
    - "measuring i2p censorship at a global scale"
    - "tranco a research oriented top sites ranking hardened against manipulation"
    - "an empirical study of the i2p anonymity network and its censorship resistance"
    - "a long way to the top significance structure and stability of internet top lists"
    - "hiding in plain sight a longitudinal study of combosquatting abuse"
    - "your neighbors are my spies location and other privacy concerns in glbt focused location based dating applications"

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"
    - "automated website fingerprinting through deep learning"
    - "how unique is your onion an analysis of the fingerprintability of tor onion services"
    - "toward an efficient website fingerprinting defense"

Connected through hyperedge HE-7 (induced by source paper "pacer network side channel mitigation in the cloud"):
  with members:
    - "measuring information leakage in website fingerprinting attacks and defenses"
    - "netspectre read arbitrary memory over network"
    - "obladi oblivious serializable transactions in the cloud"
    - "loophole timing attacks on shared event loops in chrome"
    - "mitigating timing side channel in shared schedulers"
    - "robust and efficient elimination of cache and timing side channels"
    - "drama exploiting dram addressing for cross cpu attacks"
    - "deterministically deterring timing attacks in deterland"

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"
    - "deep fingerprinting undermining website fingerprinting defenses with deep learning"
    - "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"
    - "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)