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 "your smart home can t keep a secret towards automated fingerprinting of iot traffic with neural networks"):
  Abstract: The IoT (Internet of Things) technology has been widely adopted in recent years and has profoundly changed the people's daily lives. However, in the meantime, such a fast-growing technology has also introduced new privacy issues, which need to be better understood and measured. In this work, we look into how private information can be leaked from network traffic generated in the smart home network. Although researchers have proposed techniques to infer IoT device types or user behaviors under clean experiment setup, the effectiveness of such approaches become questionable in the complex but realistic network environment, where common techniques like Network Address and Port Translation (NAPT) and Virtual Private Network (VPN) are enabled. Traffic analysis using traditional methods (e.g., through classical machine-learning models) is much less effective under those settings, as the features picked manually are not distinctive any more. In this work, we propose a traffic analysis framework based on sequence-learning techniques like LSTM and leveraged the temporal relations between packets for the attack of device identification. We evaluated it under different environment settings (e.g., pure-IoT and noisy environment with multiple non-IoT devices). The results showed our framework was able to differentiate device types with a high accuracy. This result suggests IoT network communications pose prominent challenges to users' privacy, even when they are protected by encryption and morphed by the network gateway. As such, new privacy protection methods on IoT traffic need to be developed towards mitigating this new issue.

Includes members:
  Member "deep fingerprinting undermining website fingerprinting defenses with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blind adversarial network perturbations")
  Member "kitsune an ensemble of autoencoders for online network intrusion detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "text a 3 activation anomaly analysis")
  Member "peek a boo i see your smart home activities even encrypted":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "iot behavioral monitoring via network traffic analysis")
  Member "automated website fingerprinting through deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "blind adversarial network perturbations")
  Member "a smart home is no castle privacy vulnerabilities of encrypted iot traffic":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "iot behavioral monitoring via network traffic analysis")
  Member "spying on the smart home privacy attacks and defenses on encrypted iot traffic":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "et tu alexa when commodity wifi devices turn into adversarial motion sensors")
  Member "iotscanner detecting and classifying privacy threats in iot neighborhoods":
    also connected through other hyperedges:
      (none)
  Member "iot sentinel demo automated device type identification for security enforcement in iot":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "iot behavioral monitoring via network traffic analysis")

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)