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: "abstract hidden markov models a monadic account of quantitative information flow"
  Abstract: Hidden Markov Models, HMM's, are mathematical models of Markov processes with#N#state that is hidden, but from which information can leak. They are typically#N#represented as 3-way joint-probability distributions.#N#  We use HMM's as denotations of probabilistic hidden-state sequential#N#programs: for that, we recast them as `abstract' HMM's, computations in the#N#Giry monad $\mathbb{D}$, and we equip them with a partial order of increasing#N#security. However to encode the monadic type with hiding over some state#N#$\mathcal{X}$ we use $\mathbb{D}\mathcal{X}\to \mathbb{D}^2\mathcal{X}$ rather#N#than the conventional $\mathcal{X}{\to}\mathbb{D}\mathcal{X}$ that suffices for#N#Markov models whose state is not hidden. We illustrate the#N#$\mathbb{D}\mathcal{X}\to \mathbb{D}^2\mathcal{X}$ construction with a small#N#Haskell prototype.#N#  We then present uncertainty measures as a generalisation of the extant#N#diversity of probabilistic entropies, with characteristic analytic properties#N#for them, and show how the new entropies interact with the order of increasing#N#security. Furthermore, we give a `backwards' uncertainty-transformer semantics#N#for HMM's that is dual to the `forwards' abstract HMM's - it is an analogue of#N#the duality between forwards, relational semantics and backwards,#N#predicate-transformer semantics for imperative programs with demonic choice.#N#  Finally, we argue that, from this new denotational-semantic viewpoint, one#N#can see that the Dalenius desideratum for statistical databases is actually an#N#issue in compositionality. We propose a means for taking it into account.

Connected through hyperedge HE-1 (induced by source paper "distances between states and between predicates"):
  with members:
    - "mixed powerdomains for probability and nondeterminism"
    - "disintegration and bayesian inversion both abstractly and concretely"
    - "a recipe for state and effect triangles"
    - "an introduction to effectus theory"
    - "behavioral metrics via functor lifting"

Connected through hyperedge HE-2 (induced by source paper "hyper normalisation and conditioning for discrete probability distributions"):
  with members:
    - "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"
    - "compositional closure for bayes risk in probabilistic noninterference"

Connected through hyperedge HE-3 (induced by source paper "compositional security and collateral leakage"):
  with members:
    - "hidden markov program algebra with iteration"
    - "compositional closure for bayes risk in probabilistic noninterference"

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.LO(Logic in Computer Science)