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: "semantics for probabilistic programming higher order functions continuous distributions and soft constraints"
  Abstract: We study the semantic foundation of expressive probabilistic programming languages, that support higher-order functions, continuous distributions, and soft constraints (such as Anglican, Church, and Venture). We define a metalanguage (an idealised version of Anglican) for probabilistic computation with the above features, develop both operational and denotational semantics, and prove soundness, adequacy, and termination. They involve measure theory, stochastic labelled transition systems, and functor categories, but admit intuitive computational readings, one of which views sampled random variables as dynamically allocated read-only variables. We apply our semantics to validate nontrivial equations underlying the correctness of certain compiler optimisations and inference algorithms such as sequential Monte Carlo simulation. The language enables defining probability distributions on higher-order functions, and we study their properties.

Connected through hyperedge HE-1 (induced by source paper "etalumis bringing probabilistic programming to scientific simulators at scale"):
  with members:
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "pyro deep universal probabilistic programming"
    - "an empirical model of large batch training"
    - "measuring the effects of data parallelism on neural network training"
    - "a comprehensive study of batch construction strategies for recurrent neural networks in mxnet"
    - "scaling sgd batch size to 32k for imagenet training"
    - "carla an open urban driving simulator"
    - "improvements to inference compilation for probabilistic programming in large scale scientific simulators"

Connected through hyperedge HE-2 (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"
    - "a convenient category for higher order probability theory"
    - "denotational validation of higher order bayesian inference"
    - "cantor meets scott semantic foundations for probabilistic networks"
    - "deriving probability density functions from probabilistic functional programs"

Connected through hyperedge HE-3 (induced by source paper "deployable probabilistic programming"):
  with members:
    - "probabilistic programming with densities in slicstan efficient flexible and deterministic"
    - "don t unroll adjoint differentiating ssa form programs"
    - "pyro deep universal probabilistic programming"
    - "composing modeling and inference operations with probabilistic program combinators"
    - "automatic differentiation in ml where we are and where we should be going"
    - "fashionable modelling with flux"
    - "tangent automatic differentiation using source code transformation for dynamically typed array programming"
    - "design and implementation of probabilistic programming language anglican"

Connected through hyperedge HE-4 (induced by source paper "probabilistic programming with densities in slicstan efficient flexible and deterministic"):
  with members:
    - "a convenient category for higher order probability theory"
    - "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-5 (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"
    - "a convenient category for higher order probability theory"
    - "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-6 (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"
    - "a convenient category for higher order probability theory"
    - "denotational validation of higher order bayesian inference"
    - "a lambda calculus foundation for universal probabilistic programming"

Connected through hyperedge HE-7 (induced by source paper "approximations in probabilistic programs"):
  with members:
    - "verifying handcoded probabilistic inference procedures"
    - "denotational validation of higher order bayesian inference"
    - "a lambda calculus foundation for universal probabilistic programming"
    - "church a language for generative models"

Connected through hyperedge HE-8 (induced by source paper "a linear exponential comonad in s finite transition kernels and probabilistic coherent spaces"):
  with members:
    - "geometry of interaction for mall via hughes vanglabbeek proof nets"
    - "full abstraction for probabilistic pcf"
    - "measure transformer semantics for bayesian machine learning"

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)