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: "a convenient category for higher order probability theory"
  Abstract: Higher-order probabilistic programming languages allow programmers to write sophisticated models in machine learning and statistics in a succinct and structured way, but step outside the standard measure-theoretic formalization of probability theory. Programs may use both higher-order functions and continuous distributions, or even define a probability distribution on functions. But standard probability theory does not handle higher-order functions well: the category of measurable spaces is not cartesian closed. #R##N#Here we introduce quasi-Borel spaces. We show that these spaces: form a new formalization of probability theory replacing measurable spaces; form a cartesian closed category and so support higher-order functions; form a well-pointed category and so support good proof principles for equational reasoning; and support continuous probability distributions. We demonstrate the use of quasi-Borel spaces for higher-order functions and probability by: showing that a well-known construction of probability theory involving random functions gains a cleaner expression; and generalizing de Finetti's theorem, that is a crucial theorem in probability theory, to quasi-Borel spaces.

Connected through hyperedge HE-1 (induced by source paper "towards verified stochastic variational inference for probabilistic programs"):
  with members:
    - "pyro deep universal probabilistic programming"
    - "discrete continuous mixtures in probabilistic programming generalized semantics and inference algorithms"
    - "a domain theory for statistical probabilistic programming"
    - "existence and continuity of differential entropy for a class of distributions"
    - "denotational validation of higher order bayesian inference"
    - "cantor meets scott semantic foundations for probabilistic networks"
    - "deriving probability density functions from probabilistic functional programs"
    - "semantics for probabilistic programming higher order functions continuous distributions and soft constraints"

Connected through hyperedge HE-2 (induced by source paper "formal verification of higher order probabilistic programs reasoning about approximation convergence bayesian inference and optimization"):
  with members:
    - "equivalence between wasserstein and value aware model based reinforcement learning"
    - "relational reasoning for markov chains in a probabilistic guarded lambda calculus"
    - "measurable cones and stable measurable functions a model for probabilistic higher order programming"
    - "mixed powerdomains for probability and nondeterminism"
    - "denotational validation of higher order bayesian inference"
    - "a theory of slicing for probabilistic control flow graphs"
    - "a relational logic for higher order programs"
    - "a program logic for union bounds"

Connected through hyperedge HE-3 (induced by source paper "probabilistic programming with densities in slicstan efficient flexible and deterministic"):
  with members:
    - "semantics for probabilistic programming higher order functions continuous distributions and soft constraints"
    - "tensorflow a system for large scale machine learning"
    - "bayesdb a probabilistic programming system for querying the probable implications of data"
    - "a lambda calculus foundation for universal probabilistic programming"
    - "running probabilistic programs backwards"
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "measure transformer semantics for bayesian machine learning"
    - "church a language for generative models"

Connected through hyperedge HE-4 (induced by source paper "compositional inference metaprogramming with convergence guarantees"):
  with members:
    - "denotational validation of higher order bayesian inference"
    - "swift compiled inference for probabilistic programming languages"
    - "a lambda calculus foundation for universal probabilistic programming"
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "church a language for generative models"
    - "gibbs sampling in open universe stochastic languages"
    - "probabilistic operational semantics for the lambda calculus"

Connected through hyperedge HE-5 (induced by source paper "the geometry of bayesian programming"):
  with members:
    - "formal verification of higher order probabilistic programs reasoning about approximation convergence bayesian inference and optimization"
    - "a domain theory for statistical probabilistic programming"
    - "measurable cones and stable measurable functions a model for probabilistic higher order programming"
    - "the geometry of parallelism classical probabilistic and quantum effects"
    - "a lambda calculus foundation for universal probabilistic programming"
    - "full abstraction for probabilistic pcf"
    - "church a language for generative models"

Connected through hyperedge HE-6 (induced by source paper "synthetic topology in homotopy type theory for probabilistic programming"):
  with members:
    - "an application of computable distributions to the semantics of probabilistic programs"
    - "formalising real numbers in homotopy type theory"
    - "stack semantics of type theory"
    - "semantics for probabilistic programming higher order functions continuous distributions and soft constraints"
    - "the hott library a formalization of homotopy type theory in coq"
    - "partiality revisited the partiality monad as a quotient inductive inductive type"
    - "quotient inductive inductive types"

Connected through hyperedge HE-7 (induced by source paper "semantics of higher order probabilistic programs with conditioning"):
  with members:
    - "borel kernels and their approximation categorically"
    - "a domain theory for statistical probabilistic programming"
    - "measurable cones and stable measurable functions a model for probabilistic higher order programming"
    - "denotational validation of higher order bayesian inference"
    - "semantics for probabilistic programming higher order functions continuous distributions and soft constraints"
    - "a lambda calculus foundation for universal probabilistic programming"

Connected through hyperedge HE-8 (induced by source paper "the theory of traces for systems with nondeterminism and probability"):
  with members:
    - "trace and testing metrics on nondeterministic probabilistic processes"
    - "convex language semantics for nondeterministic probabilistic automata"
    - "mixed powerdomains for probability and nondeterminism"
    - "the power of convex algebras"
    - "termination in convex sets of distributions"
    - "layer by layer combining monads"
    - "distribution based bisimulation for labelled markov processes"
    - "a storm is coming a modern probabilistic model checker"

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.PL(Programming Languages)