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: "a study of mac address randomization in mobile devices and when it fails"
  Abstract: MAC address randomization is a privacy technique whereby mobile devices rotate through random hardware addresses in order to prevent observers from singling out their traffic or physical location from other nearby devices. Adoption of this technology, however, has been sporadic and varied across device manufacturers. In this paper, we present the first wide-scale study of MAC address randomization in the wild, including a detailed breakdown of different randomization techniques by operating system, manufacturer, and model of device. #R##N#We then identify multiple flaws in these implementations which can be exploited to defeat randomization as performed by existing devices. First, we show that devices commonly make improper use of randomization by sending wireless frames with the true, global address when they should be using a randomized address. We move on to extend the passive identification techniques of Vanhoef et al. to effectively defeat randomization in ~96% of Android phones. Finally, we show a method that can be used to track 100% of devices using randomization, regardless of manufacturer, by exploiting a previously unknown flaw in the way existing wireless chipsets handle low-level control frames.

Connected through hyperedge HE-1 (induced by source paper "nowhere to hide cross modal identity leakage between biometrics and devices"):
  with members:
    - "autonomous learning for face recognition in the wild via ambient wireless cues"
    - "voxceleb2 deep speaker recognition"
    - "systematic classification of side channel attacks a case study for mobile devices"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "trajectory recovery from ash user privacy is not preserved in aggregated mobility data"
    - "armageddon cache attacks on mobile devices"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-2 (induced by source paper "et tu alexa when commodity wifi devices turn into adversarial motion sensors"):
  with members:
    - "peek a boo i see your smart home activities even encrypted"
    - "spying on the smart home privacy attacks and defenses on encrypted iot traffic"
    - "position tracking for virtual reality using commodity wifi"
    - "wi fi gesture recognition on existing devices"

Connected through hyperedge HE-3 (induced by source paper "understanding crowd behaviors in a social event by passive wifi sensing and data mining"):
  with members:
    - "understanding urban human mobility through crowdsensed data"
    - "an analysis of visitors behavior in the louvre museum a study using bluetooth data"

Connected through hyperedge HE-4 (induced by source paper "handoff all your privacy a review of apple s bluetooth low energy continuity protocol"):
  with members:
    - "sundials in the shade an internet wide perspective on icmp timestamps"
    - "easy 4g lte imsi catchers for non programmers"
    - "passive taxonomy of wifi clients using mlme frame contents"
    - "device fingerprinting in wireless networks challenges and opportunities"
    - "tracking human mobility using wifi signals"
    - "an empirical study of passive 802 11 device fingerprinting"

Connected through hyperedge HE-5 (induced by source paper "adversarial wifi sensing using a single smartphone"):
  with members:
    - "position tracking for virtual reality using commodity wifi"
    - "back to the drawing board revisiting the design of optimal location privacy preserving mechanisms"
    - "powerspy location tracking using mobile device power analysis"
    - "protecting locations with differential privacy under temporal correlations"
    - "optimal geo indistinguishable mechanisms for location privacy"

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)