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 "android inter app communication threats and detection techniques"):
  Abstract: Abstract   With the digital breakthrough, smart phones have become very essential component for many routine tasks like shopping, paying bills, transferring money, instant messaging, emails etc. Mobile devices are very attractive attack surface for cyber thieves as they hold personal details (accounts, locations, contacts, photos) and have potential capabilities for eavesdropping (with cameras/microphone, wireless connections). Android, being the most popular, is the target of malicious hackers who are trying to use Android app as a tool to break into and control device. Android malware authors use many anti-analysis techniques to hide from analysis tools. Academic researchers and commercial anti-malware companies are putting great effort to detect such malicious apps. They are making use of the combinations of static, dynamic and behavior-based analysis techniques.  Despite of all the security mechanisms provided by Android, apps can carry out malicious actions through inter-app communication. One such inter-app communication threats is collusion. In collusion, malicious functionality is divided across multiple apps. Each participating app accomplishes its part and communicate information to another app through Inter Component Communication (ICC). ICC does not require any special permissions. Also there is no compulsion to inform user about the communication. Each participating app needs to request a minimal set of privileges, which may make it appear benign to current state-of-the-art techniques that analyze one app at a time.  There are many surveys on app analysis techniques in Android; however they focus on single-app analysis. This survey highlights several inter-app communication threats, in particular collusion among multiple-apps. In this paper, we present Android vulnerabilities that may be exploited for carrying privilege escalation attacks, privacy leakage and collusion attacks. We cover the existing threat analysis, scenarios, and a detailed comparison of tools for intra- and inter-app analysis. To the best of our knowledge this is the first survey on inter-app communication threats, app collusion and state-of-the-art detection tools in Android.

Includes members:
  Member "towards automated android app collusion detection":
    also connected through other hyperedges:
      (none)
  Member "android security framework enabling generic and extensible access control on android":
    also connected through other hyperedges:
      (none)
  Member "cross site scripting attacks on android webview":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "babelview evaluating the impact of code injection attacks in mobile webviews")
  Member "a case of collusion a study of the interface between ad libraries and their apps":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "demo recon revealing and controlling pii leaks in mobile network traffic")
  Member "adsplit separating smartphone advertising from applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an empirical study of usages updates and risks of third party libraries in java projects")
  Member "dexpler converting android dalvik bytecode to jimple for static analysis with soot":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mobile app analysis and instrumentation techniques reimagined with decree")
  Member "quire lightweight provenance for smart phone operating systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secloak arm trustzone based mobile peripheral control")

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)