human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "analyzing https traffic for a robust identification of operating system browser and application"):
  Abstract: Desktops and laptops can be maliciously exploited to violate privacy. There are two main types of attack scenarios: active and passive. In this paper, we consider the passive scenario where the adversary does not interact actively with the device, but he is able to eavesdrop on the network traffic of the device from the network side. Most of the Internet traffic is encrypted and thus passive attacks are challenging. In this paper, we show that an external attacker can robustly identify the operating system, browser and application of HTTP encrypted traffic (HTTPS). We provide a large dataset of more than 20,000 examples for this task. We present a comprehensive evaluation of traffic features including new ones and machine learning algorithms. We run a comprehensive set of experiments, which shows that our classification accuracy is 96.06%. Due to the adaptive nature of the problem, we also investigate the robustness and resilience to changes of features due to different network conditions (e.g., VPN) at test time and the effect of small training set on the accuracy. We show that our proposed solution is robust to these changes.

Includes members:
  Member "i know what you saw last minute encrypted http adaptive video streaming title classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "prifi low latency anonymity for organizational networks")
  Member "real time video quality representation classification of encrypted http adaptive video streaming the case of safari":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "perceived audiovisual quality modelling based on decison trees genetic programming and neural networks")

Question: Please tell me which class the source paper 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)