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 "undermining user privacy on mobile devices using ai"):
  Abstract: Over the past years, literature has shown that attacks exploiting the microarchitecture of modern processors pose a serious threat to the privacy of mobile phone users. This is because applications leave distinct footprints in the processor, which can be used by malware to infer user activities. In this work, we show that these inference attacks are considerably more practical when combined with advanced AI techniques. In particular, we focus on profiling the activity in the last-level cache (LLC) of ARM processors. We employ a simple Prime+Probe based monitoring technique to obtain cache traces, which we classify with Deep Learning methods including Convolutional Neural Networks. We demonstrate our approach on an off-the-shelf Android phone by launching a successful attack from an unprivileged, zeropermission App in well under a minute. The App thereby detects running applications with an accuracy of 98% and reveals opened websites and streaming videos by monitoring the LLC for at most 6 seconds. This is possible, since Deep Learning compensates measurement disturbances stemming from the inherently noisy LLC monitoring and unfavorable cache characteristics such as random line replacement policies. In summary, our results show that thanks to advanced AI techniques, inference attacks are becoming alarmingly easy to implement and execute in practice. This once more calls for countermeasures that confine microarchitectural leakage and protect mobile phone applications, especially those valuing the privacy of their users.

Includes members:
  Member "deepcloak adversarial crafting as a defensive measure to cloak processes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "defending against machine learning based inference attacks via adversarial examples opportunities and challenges")
  Member "theory and practice of finding eviction sets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "store to leak forwarding leaking data on meltdown resistant cpus")
  Member "loophole timing attacks on shared event loops in chrome":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "revisiting and evaluating software side channel vulnerabilities and countermeasures in cryptographic applications")
  Member "perfweb how to violate web privacy with hardware performance events":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "jackhammer efficient rowhammer on heterogeneous fpga cpu platforms")
  Member "autolock why cache attacks on arm are harder than you think":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sok hardware security support for trustworthy execution")
  Member "memory dos attacks in multi tenant clouds severity and mitigation":
    also connected through other hyperedges:
      (none)
  Member "flush flush a fast and stealthy cache attack":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "copycat controlled instruction level attacks on enclaves for maximal key extraction")
  Member "tensorflow a system for large scale machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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.CR(Cryptography and Security)