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: "compositional closure for bayes risk in probabilistic noninterference"
  Abstract: We give a sequential model for noninterference security including probability (but not demonic choice), thus supporting reasoning about the likelihood that high-security values might be revealed by observations of low-security activity. Our novel methodological contribution is the definition of a refinement order and its use to compare security measures between specifications and (their supposed) implementations. This contrasts with the more common practice of evaluating the security of individual programs in isolation. #R##N#The appropriateness of our model and order is supported by our showing that our refinement order is the greatest compositional relation --the compositional closure-- with respect to our semantics and an "elementary" order based on Bayes Risk --- a security measure already in widespread use. We also relate refinement to other measures such as Shannon Entropy. #R##N#By applying the approach to a non-trivial example, the anonymous-majority Three-Judges protocol, we demonstrate by example that correctness arguments can be simplified by the sort of layered developments --through levels of increasing detail-- that are allowed and encouraged by compositional semantics.

Connected through hyperedge HE-1 (induced by source paper "generating optimal privacy protection mechanisms via machine learning"):
  with members:
    - "privacy preserving adversarial networks"
    - "back to the drawing board revisiting the design of optimal location privacy preserving mechanisms"
    - "differential privacy as a mutual information constraint"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "privacy games optimal user centric data obfuscation"
    - "optimal geo indistinguishable mechanisms for location privacy"
    - "geo indistinguishability differential privacy for location based systems"
    - "on an improvement over renyi s equivocation bound"

Connected through hyperedge HE-2 (induced by source paper "abstract hidden markov models a monadic account of quantitative information flow"):
  with members:
    - "hidden markov program algebra with iteration"

Connected through hyperedge HE-3 (induced by source paper "metric based local differential privacy for statistical applications"):
  with members:
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "extremal mechanisms for local differential privacy"
    - "geo indistinguishability differential privacy for location based systems"
    - "de anonymizing social networks"

Connected through hyperedge HE-4 (induced by source paper "an algebraic approach for reasoning about information flow"):
  with members:
    - "leakage and protocol composition in a game theoretic perspective"
    - "on the compositionality of quantitative information flow"
    - "quantitative information flow under generic leakage functions and adaptive adversaries"

Connected through hyperedge HE-5 (induced by source paper "information leakage games"):
  with members:
    - "quantitative information flow under generic leakage functions and adaptive adversaries"
    - "type based self stabilisation for computational fields"
    - "security games with information leakage modeling and computation"
    - "projection onto the probability simplex an efficient algorithm with a simple proof and an application"
    - "calibrating data to sensitivity in private data analysis"
    - "stackelberg vs nash in security games an extended investigation of interchangeability equivalence and uniqueness"

Connected through hyperedge HE-6 (induced by source paper "hyper normalisation and conditioning for discrete probability distributions"):
  with members:
    - "abstract hidden markov models a monadic account of quantitative information flow"
    - "semantics for probabilistic programming higher order functions continuous distributions and soft constraints"
    - "a type theory for probabilistic and bayesian reasoning"
    - "hidden markov program algebra with iteration"
    - "measure transformer semantics for bayesian machine learning"

Connected through hyperedge HE-7 (induced by source paper "compositional security and collateral leakage"):
  with members:
    - "abstract hidden markov models a monadic account of quantitative information flow"
    - "hidden markov program algebra with iteration"

Connected through hyperedge HE-8 (induced by source paper "hidden markov program algebra with iteration"):
  with members:
    - "quantitative information flow verification hardness and possibilities"

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.FL(Formal Languages and Automata Theory)