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: "robust smartphone app identification via encrypted network traffic analysis"
  Abstract: The apps installed on a smartphone can reveal much information about a user, such as their medical conditions, sexual orientation, or religious beliefs. Additionally, the presence or absence of particular apps on a smartphone can inform an adversary who is intent on attacking the device. In this paper, we show that a passive eavesdropper can feasibly identify smartphone apps by fingerprinting the network traffic that they send. Although SSL/TLS hides the payload of packets, side-channel data such as packet size and direction is still leaked from encrypted connections. We use machine learning techniques to identify smartphone apps from this side-channel data. In addition to merely fingerprinting and identifying smartphone apps, we investigate how app fingerprints change over time, across devices and across different versions of apps. Additionally, we introduce strategies that enable our app classification system to identify and mitigate the effect of ambiguous traffic, i.e., traffic in common among apps such as advertisement traffic. We fully implemented a framework to fingerprint apps and ran a thorough set of experiments to assess its performance. We fingerprinted 110 of the most popular apps in the Google Play Store and were able to identify them six months later with up to 96% accuracy. Additionally, we show that app fingerprints persist to varying extents across devices and app versions.

Connected through hyperedge HE-1 (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"
    - "automated website fingerprinting through deep learning"
    - "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-2 (induced by source paper "large scale mobile app identification using deep learning"):
  with members:
    - "a target agnostic attack on deep models exploiting security vulnerabilities of transfer learning"
    - "multitask learning for network traffic classification"
    - "how to achieve high classification accuracy with just a few labels a semi supervised approach using sampled packets"
    - "overcoming catastrophic forgetting problem by weight consolidation and long term memory"
    - "deep learning for encrypted traffic classification an overview"
    - "the dark side channel of mobile devices a survey on network traffic analysis"
    - "can t you hear me knocking identification of user actions on android apps via traffic analysis"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "devicewatch identifying compromised mobile devices through network traffic analysis and graph inference"):
  with members:
    - "characterizing location based mobile tracking in mobile ad networks"
    - "beyond google play a large scale comparative study of chinese android app markets"
    - "the dark side channel of mobile devices a survey on network traffic analysis"
    - "demo recon revealing and controlling pii leaks in mobile network traffic"
    - "node2vec scalable feature learning for networks"
    - "personalized security indicators to detect application phishing attacks in mobile platforms"
    - "detection of early stage enterprise infection by mining large scale log data"

Connected through hyperedge HE-4 (induced by source paper "hedge efficient traffic classification of encrypted and compressed packets"):
  with members:
    - "detecting compressed cleartext traffic from consumer internet of things devices"
    - "cleartext data transmissions in consumer iot medical devices"

Connected through hyperedge HE-5 (induced by source paper "deep learning for encrypted traffic classification an overview"):
  with members:
    - "how to achieve high classification accuracy with just a few labels a semi supervised approach using sampled packets"
    - "active one shot learning"
    - "deep packet a novel approach for encrypted traffic classification using deep learning"

Connected through hyperedge HE-6 (induced by source paper "the untold secrets of operational wi fi calling services vulnerabilities attacks and countermeasures"):
  with members:
    - "don t skype type acoustic eavesdropping in voice over ip"
    - "finding tiny faces"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"

Connected through hyperedge HE-7 (induced by source paper "the dark side channel of mobile devices a survey on network traffic analysis"):
  with members:
    - "demo recon revealing and controlling pii leaks in mobile network traffic"
    - "no nat d user left behind fingerprinting users behind nat from netflow records alone"
    - "signature generation for sensitive information leakage in android applications"

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)