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 lambda calculus foundation for universal probabilistic programming"
  Abstract: We develop the operational semantics of an untyped probabilistic lambda-calculus with continuous distributions, as a foundation for universal probabilistic programming languages such as Church, Anglican, and Venture. Our first contribution is to adapt the classic operational semantics of lambda-calculus to a continuous setting via creating a measure space on terms and defining step-indexed approximations. We prove equivalence of big-step and small-step formulations of this distribution-based semantics. To move closer to inference techniques, we also define the sampling-based semantics of a term as a function from a trace of random samples to a value. We show that the distribution induced by integrating over all traces equals the distribution-based semantics. Our second contribution is to formalize the implementation technique of trace Markov chain Monte Carlo (MCMC) for our calculus and to show its correctness. A key step is defining sufficient conditions for the distribution induced by trace MCMC to converge to the distribution-based semantics. To the best of our knowledge, this is the first rigorous correctness proof for trace MCMC for a higher-order functional language.

Connected through hyperedge HE-1 (induced by source paper "tail bound analysis for probabilistic programs via central moments"):
  with members:
    - "cost analysis of nondeterministic probabilistic programs"
    - "aiming low is harder inductive proof rules for lower bounds on weakest preexpectations in probabilistic program verification"
    - "bounded expectations resource analysis for probabilistic programs"
    - "termination of nondeterministic recursive probabilistic programs"
    - "termination analysis of probabilistic programs through positivstellensatz s"
    - "weakest precondition reasoning for expected run times of probabilistic programs"
    - "reasoning about recursive probabilistic programs"

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 "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"
    - "a convenient category for higher order probability theory"
    - "denotational validation of higher order bayesian inference"
    - "a theory of slicing for probabilistic control flow graphs"
    - "a relational logic for higher order programs"

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"
    - "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"
    - "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 "lambda calculus and probabilistic computation"):
  with members:
    - "the bang calculus and the two girard s translations"
    - "confluence in probabilistic rewriting"
    - "on probabilistic term rewriting"
    - "the geometry of parallelism classical probabilistic and quantum effects"
    - "toward general analysis of recursive probability models"
    - "church a language for generative models"
    - "probabilistic operational semantics for the lambda calculus"
    - "confluence results for a quantum lambda calculus with measurements"

Connected through hyperedge HE-6 (induced by source paper "compositional inference metaprogramming with convergence guarantees"):
  with members:
    - "a convenient category for higher order probability theory"
    - "denotational validation of higher order bayesian inference"
    - "swift compiled inference for probabilistic programming languages"
    - "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-7 (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"
    - "a convenient category for higher order probability theory"
    - "the geometry of parallelism classical probabilistic and quantum effects"
    - "full abstraction for probabilistic pcf"
    - "church a language for generative models"

Connected through hyperedge HE-8 (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"
    - "semantics for probabilistic programming higher order functions continuous distributions and soft constraints"

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)