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 "compositional security and collateral leakage"):
  Abstract: In quantitative information flow we say that program Q is “at least as secure as” P just when the amount of secret information flowing from Q is never more than flows from P , with of course a suitable quantification of “flow”. This secure-refinement order v is compositional just when PvQ implies C(P )vC(Q) for any context C, again with a suitable definition of “context”. Remarkable however is that leaks caused by executing P,Q might not be limited to their declared variables: they might impact correlated secrets in variables declared and initialised in some broader context to which P,Q do not refer even implicitly. We call such leaks collateral because their effect is felt in domains of which (the programmers of) P,Q might be wholly unaware: our inspiration is the “Dalenius” phenomenon for statistical databases [9, 11]. We show that a proper treatment of these collateral leaks is necessary for a compositional program semantics for read/write “open” programs. By adapting a recent Hidden-Markov denotational model for non-interference security [21, 22], so that it becomes “collateral aware”, we give techniques and examples (e.g. public-key encryption) to show how collateral leakage can be calculated and then bounded in its severity.

Includes members:
  Member "abstract hidden markov models a monadic account of quantitative information flow":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distances between states and between predicates")
  Member "hidden markov program algebra with iteration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "abstract hidden markov models a monadic account of quantitative information flow")
  Member "compositional closure for bayes risk in probabilistic noninterference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generating optimal privacy protection mechanisms via machine learning")

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)