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 "large scale mobile app identification using deep learning"):
  Abstract: Many network services and tools (e.g. network monitors, malware-detection systems, routing and billing policy enforcement modules in ISPs) depend on identifying the type of traffic that passes through the network. With the widespread use of mobile devices, the vast diversity of mobile apps, and the massive adoption of encryption protocols (such as TLS), large-scale traffic classification becomes inevitable and more difficult. In this paper, we propose a deep learning model for mobile app identification. The proposed model only needs the payload of the first few packets for classification, and, hence, it is suitable even for applications that rely on early prediction, such as routing and QoS provisioning. The deep model achieves between 84% to 98% accuracy for the identification of 80 popular apps. We also perform occlusion analysis for the first time to bring insight into what data is leaked from SSL/TLS protocol that allows accurate app identification. Moreover, our traffic analysis shows that many apps generate not only app-specific traffic, but also numerous ambiguous flows. Ambiguous flows are flows generated by common functionality modules, such as advertisement and traffic analytics. Because such flows are common among many different apps, identifying the source app that generates ambiguous flows is challenging. To address this challenge, we propose a CNN+LSTM model that takes adjacent flows to learn the order and pattern of multiple flows, to better identify the app that generates them. We show that such flow association considerably improves the accuracy, particularly for ambiguous flows. Furthermore, we show that our approach is robust to mixed traffic scenarios where some unrelated flows may appear in adjacent flows. To the best of our knowledge, this is the first work that identifies the source app for ambiguous flows.

Includes members:
  Member "a target agnostic attack on deep models exploiting security vulnerabilities of transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improving the adversarial robustness of transfer learning via noisy feature distillation")
  Member "multitask learning for network traffic classification":
    also connected through other hyperedges:
      (none)
  Member "how to achieve high classification accuracy with just a few labels a semi supervised approach using sampled packets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial network traffic toward evaluating the robustness of deep learning based network traffic classification")
  Member "overcoming catastrophic forgetting problem by weight consolidation and long term memory":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multitask learning for network traffic classification")
  Member "deep learning for encrypted traffic classification an overview":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a target agnostic attack on deep models exploiting security vulnerabilities of transfer learning")
  Member "the dark side channel of mobile devices a survey on network traffic analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "devicewatch identifying compromised mobile devices through network traffic analysis and graph inference")
  Member "robust smartphone app identification via encrypted network traffic analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial network traffic toward evaluating the robustness of deep learning based network traffic classification")
  Member "can t you hear me knocking identification of user actions on android apps via traffic analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bicycle attacks considered harmful quantifying the damage of widespread password length leakage")

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.NI(Networking and Internet Architecture)