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 "hybrid statistical estimation of mutual information and its application to information flow"):
  Abstract: Analysis of a probabilistic system often requires to learn the joint probability distribution of its random variables. The computation of the exact distribution is usually an exhaustive precise analysis on all executions of the system. To avoid the high computational cost of such an exhaustive search, statistical analysis has been studied to efficiently obtain approximate estimates by analyzing only a small but representative subset of the system’s behavior. In this paper we propose a hybrid statistical estimation method that combines precise and statistical analyses to estimate mutual information, Shannon entropy, and conditional entropy, together with their confidence intervals. We show how to combine the analyses on different components of a discrete system with different accuracy to obtain an estimate for the whole system. The new method performs weighted statistical analysis with different sample sizes over different components and dynamically finds their optimal sample sizes. Moreover, it can reduce sample sizes by using prior knowledge about systems and a new abstraction-then-sampling technique based on qualitative analysis. To apply the method to the source code of a system, we show how to decompose the code into components and to determine the analysis method for each component by overviewing the implementation of those techniques in the HyLeak tool. We demonstrate with case studies that the new method outperforms the state of the art in quantifying information leakage.

Includes members:
  Member "what s the over under probabilistic bounds on information leakage":
    also connected through other hyperedges:
      (none)
  Member "a game theoretic approach to information flow control via protocol composition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "verification and control of turn based probabilistic real time games")
  Member "information leakage games":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "game theoretical modelling of network cyber security review paper")
  Member "on the compositionality of quantitative information flow":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "local obfuscation mechanisms for hiding probability distributions")
  Member "quantitative information flow for scheduler dependent systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "quantitative aspects of programming languages and systems over the past 2 4 years and beyond")
  Member "speeding up smt based quantitative program analysis":
    also connected through other hyperedges:
      (none)
  Member "a scalable approximate model counter":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "quantitative verification of neural networks and its security applications")
  Member "quantitative information flow as safety and liveness hyperproperties":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "quantitative aspects of programming languages and systems over the past 2 4 years and beyond")

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.IT(Information Theory)