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: "var cnn a data efficient website fingerprinting attack based on deep learning"
  Abstract: In recent years, there have been several works that use website fingerprinting techniques to enable a local adversary to determine which website a Tor user visits. While the current state-of-the-art attack, which uses deep learning, outperforms prior art with medium to large amounts of data, it attains marginal to no accuracy improvements when both use small amounts of training data. In this work, we propose Var-CNN, a website fingerprinting attack that leverages deep learning techniques along with novel insights specific to packet sequence classification. In open-world settings with large amounts of data, Var-CNN attains over $1\%$ higher true positive rate (TPR) than state-of-the-art attacks while achieving $4\times$ lower false positive rate (FPR). Var-CNN's improvements are especially notable in low-data scenarios, where it reduces the FPR of prior art by $3.12\%$ while increasing the TPR by $13\%$. Overall, insights used to develop Var-CNN can be applied to future deep learning based attacks, and substantially reduce the amount of training data needed to perform a successful website fingerprinting attack. This shortens the time needed for data collection and lowers the likelihood of having data staleness issues.

Connected through hyperedge HE-1 (induced by source paper "mockingbird defending against deep learning based website fingerprinting attacks with adversarial traces"):
  with members:
    - "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"
    - "magnet and efficient defenses against adversarial attacks are not robust to adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "tik tok the utility of packet timing in website fingerprinting attacks"):
  with members:
    - "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"
    - "k fingerprinting a robust scalable website fingerprinting technique"
    - "toward an efficient website fingerprinting defense"

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)