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: "can t you hear me knocking identification of user actions on android apps via traffic analysis"
  Abstract: While smartphone usage become more and more pervasive, people start also asking to which extent such devices can be maliciously exploited as "tracking devices". The concern is not only related to an adversary taking physical or remote control of the device (e.g., via a malicious app), but also to what a passive adversary (without the above capabilities) can observe from the device communications. Work in this latter direction aimed, for example, at inferring the apps a user has installed on his device, or identifying the presence of a specific user within a network. #R##N#In this paper, we move a step forward: we investigate to which extent it is feasible to identify the specific actions that a user is doing on his mobile device, by simply eavesdropping the device's network traffic. In particular, we aim at identifying actions like browsing someone's profile on a social network, posting a message on a friend's wall, or sending an email. We design a system that achieves this goal starting from encrypted TCP/IP packets: it works through identification of network flows and application of machine learning techniques. We did a complete implementation of this system and run a thorough set of experiments, which show that it can achieve accuracy and precision higher than 95%, for most of the considered actions.

Connected through hyperedge HE-1 (induced by source paper "bicycle attacks considered harmful quantifying the damage of widespread password length leakage"):
  with members:
    - "spaced repetition and mnemonics enable recall of multiple strong passwords"
    - "cash a cost asymmetric secure hash algorithm for optimal password protection"
    - "naturally rehearsing passwords"
    - "calibrating data to sensitivity in private data analysis"
    - "de anonymizing social networks"

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"
    - "robust smartphone app identification via encrypted network traffic analysis"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "peel the onion recognition of android apps behind the tor network"):
  with members:
    - "stealthy traffic analysis of low latency anonymous communication using throughput fingerprinting"

Connected through hyperedge HE-4 (induced by source paper "forensics analysis of android mobile voip apps"):
  with members:
    - "windows instant messaging app forensics facebook and skype as case studies"
    - "on the feasibility of attribute based encryption on smartphone devices"
    - "a forensically sound adversary model for mobile devices"
    - "forensic taxonomy of popular android mhealth apps"
    - "mobile cloud forensics an analysis of seven popular android apps"
    - "conceptual evidence collection and analysis methodology for android devices"
    - "privacy risks in mobile dating apps"
    - "forensic analysis of whatsapp messenger on android smartphones"

Connected through hyperedge HE-5 (induced by source paper "no place to hide that bytes won t reveal sniffing location based encrypted traffic to track a user s position"):
  with members:
    - "no nat d user left behind fingerprinting users behind nat from netflow records alone"
    - "geo indistinguishability differential privacy for location based systems"

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)