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: "denotational validation of higher order bayesian inference"
  Abstract: We present a modular semantic account of Bayesian inference algorithms for probabilistic programming languages, as used in data science and machine learning. Sophisticated inference algorithms are often explained in terms of composition of smaller parts. However, neither their theoretical justification nor their implementation reflects this modularity. We show how to conceptualise and analyse such inference algorithms as manipulating intermediate representations of probabilistic programs using higher-order functions and inductive types, and their denotational semantics. Semantic accounts of continuous distributions use measurable spaces. However, our use of higher-order functions presents a substantial technical difficulty: it is impossible to define a measurable space structure over the collection of measurable functions between arbitrary measurable spaces that is compatible with standard operations on those functions, such as function application. We overcome this difficulty using quasi-Borel spaces, a recently proposed mathematical structure that supports both function spaces and continuous distributions. We define a class of semantic structures for representing probabilistic programs, and semantic validity criteria for transformations of these representations in terms of distribution preservation. We develop a collection of building blocks for composing representations. We use these building blocks to validate common inference algorithms such as Sequential Monte Carlo and Markov Chain Monte Carlo. To emphasize the connection between the semantic manipulation and its traditional measure theoretic origins, we use Kock's synthetic measure theory. We demonstrate its usefulness by proving a quasi-Borel counterpart to the Metropolis-Hastings-Green theorem.

Connected through hyperedge HE-1 (induced by source paper "correctness of sequential monte carlo inference for probabilistic programming languages"):
  with members:
    - "a domain theory for statistical probabilistic programming"
    - "design and implementation of probabilistic programming language anglican"
    - "coarse to fine sequential monte carlo for 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"
    - "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-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"
    - "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-4 (induced by source paper "compositional inference metaprogramming with convergence guarantees"):
  with members:
    - "a convenient category for higher order probability theory"
    - "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 "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"
    - "semantics for probabilistic programming higher order functions continuous distributions and soft constraints"
    - "a lambda calculus foundation for universal probabilistic programming"

Connected through hyperedge HE-6 (induced by source paper "amortized rejection sampling in universal probabilistic programming"):
  with members:
    - "etalumis bringing probabilistic programming to scientific simulators at scale"
    - "efficient probabilistic inference in the quest for physics beyond the standard model"
    - "deep amortized inference for probabilistic programs"
    - "a compilation target for probabilistic programming languages"
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "church a language for generative models"

Connected through hyperedge HE-7 (induced by source paper "approximations in probabilistic programs"):
  with members:
    - "verifying handcoded probabilistic inference procedures"
    - "semantics for probabilistic programming higher order functions continuous distributions and soft constraints"
    - "a lambda calculus foundation for universal probabilistic programming"
    - "church a language for generative models"

Connected through hyperedge HE-8 (induced by source paper "a domain theory for statistical probabilistic programming"):
  with members:
    - "an application of computable distributions to the semantics of probabilistic programs"
    - "factorisation systems for logical relations and monadic lifting in type and effect system semantics"
    - "measurable cones and stable measurable functions a model for probabilistic higher order programming"
    - "a convenient category for higher order probability theory"
    - "semantics for probabilistic programming higher order functions continuous distributions and soft constraints"
    - "domains and random variables"
    - "reasoning about recursive probabilistic programs"
    - "a lambda calculus foundation for universal probabilistic programming"

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)