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: "the dark side channel of mobile devices a survey on network traffic analysis"
  Abstract: In recent years, mobile devices (e.g., smartphones and tablets) have met an increasing commercial success and have become a fundamental element of the everyday life for billions of people all around the world. Mobile devices are used not only for traditional communication activities (e.g., voice calls and messages) but also for more advanced tasks made possible by an enormous amount of multi-purpose applications (e.g., finance, gaming, and shopping). As a result, those devices generate a significant network traffic (a consistent part of the overall Internet traffic). For this reason, the research community has been investigating security and privacy issues that are related to the network traffic generated by mobile devices, which could be analyzed to obtain information useful for a variety of goals (ranging from device security and network optimization, to fine-grained user profiling). #R##N#In this paper, we review the works that contributed to the state of the art of network traffic analysis targeting mobile devices. In particular, we present a systematic classification of the works in the literature according to three criteria: (i) the goal of the analysis; (ii) the point where the network traffic is captured; and (iii) the targeted mobile platforms. In this survey, we consider points of capturing such as Wi-Fi Access Points, software simulation, and inside real mobile devices or emulators. For the surveyed works, we review and compare analysis techniques, validation methods, and achieved results. We also discuss possible countermeasures, challenges and possible directions for future research on mobile traffic analysis and other emerging domains (e.g., Internet of Things). We believe our survey will be a reference work for researchers and practitioners in this research field.

Connected through hyperedge HE-1 (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"
    - "robust smartphone app identification via encrypted 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-2 (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"
    - "robust smartphone app identification via encrypted 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-3 (induced by source paper "automatic device classification from network traffic streams of internet of things"):
  with members:
    - "detection of unauthorized iot devices using machine learning techniques"
    - "iot sentinel demo automated device type identification for security enforcement in iot"
    - "learning to diagnose with lstm recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "lightbox full stack protected stateful middlebox at lightning speed"):
  with members:
    - "deep fingerprinting undermining website fingerprinting defenses with deep learning"
    - "cachezoom how sgx amplifies the power of cache attacks"
    - "software grand exposure sgx cache attacks are practical"
    - "splitbox toward efficient private network function virtualization"
    - "private processing of outsourced network functions feasibility and constructions"

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)