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: "venture a higher order probabilistic programming platform with programmable inference"
  Abstract: We describe Venture, an interactive virtual machine for probabilistic programming that aims to be sufficiently expressive, extensible, and efficient for general-purpose use. Like Church, probabilistic models and inference problems in Venture are specified via a Turing-complete, higher-order probabilistic language descended from Lisp. Unlike Church, Venture also provides a compositional language for custom inference strategies built out of scalable exact and approximate techniques. We also describe four key aspects of Venture's implementation that build on ideas from probabilistic graphical models. First, we describe the stochastic procedure interface (SPI) that specifies and encapsulates primitive random variables. The SPI supports custom control flow, higher-order probabilistic procedures, partially exchangeable sequences and ``likelihood-free'' stochastic simulators. It also supports external models that do inference over latent variables hidden from Venture. Second, we describe probabilistic execution traces (PETs), which represent execution histories of Venture programs. PETs capture conditional dependencies, existential dependencies and exchangeable coupling. Third, we describe partitions of execution histories called scaffolds that factor global inference problems into coherent sub-problems. Finally, we describe a family of stochastic regeneration algorithms for efficiently modifying PET fragments contained within scaffolds. Stochastic regeneration linear runtime scaling in cases where many previous approaches scaled quadratically. We show how to use stochastic regeneration and the SPI to implement general-purpose inference strategies such as Metropolis-Hastings, Gibbs sampling, and blocked proposals based on particle Markov chain Monte Carlo and mean-field variational inference techniques.

Connected through hyperedge HE-1 (induced by source paper "stochastically differentiable probabilistic programs"):
  with members:
    - "deployable probabilistic programming"
    - "lf ppl a low level first order probabilistic programming language for non differentiable models"
    - "pyro deep universal probabilistic programming"
    - "design and implementation of probabilistic programming language anglican"
    - "church a language for generative models"

Connected through hyperedge HE-2 (induced by source paper "lazy object copy as a platform for population based probabilistic programming"):
  with members:
    - "pyro deep universal probabilistic programming"
    - "filtering variational objectives"
    - "design and implementation of probabilistic programming language anglican"
    - "a compilation target for probabilistic programming languages"

Connected through hyperedge HE-3 (induced by source paper "dynamicppl stan like speed for dynamic probabilistic models"):
  with members:
    - "a differentiable programming system to bridge machine learning and scientific computing"
    - "pyro deep universal probabilistic programming"
    - "julia a fresh approach to numerical computing"
    - "church a language for generative models"

Connected through hyperedge HE-4 (induced by source paper "nested reasoning about autonomous agents using probabilistic programs"):
  with members:
    - "church a language for generative models"
    - "capir collaborative action planning with intention recognition"

Connected through hyperedge HE-5 (induced by source paper "multiverse causal reasoning using importance sampling in probabilistic programming"):
  with members:
    - "counterfactual off policy evaluation with gumbel max structural causal models"
    - "universal marginalizer for amortised inference and embedding of generative models"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "pyro deep universal probabilistic programming"
    - "local rule based explanations of black box decision systems"
    - "counterfactual multi agent policy gradients"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "using synthetic data to train neural networks is model based reasoning"

Connected through hyperedge HE-6 (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-7 (induced by source paper "declarative learning based programming as an interface to ai systems"):
  with members:
    - "tensorlog deep learning meets probabilistic dbs"
    - "dynamic routing between capsules"
    - "a berkeley view of systems challenges for ai"
    - "relational learning and feature extraction by querying over heterogeneous information networks"
    - "programming with a differentiable forth interpreter"
    - "structured factored inference a framework for automated reasoning in probabilistic programming languages"
    - "tensorflow a system for large scale machine learning"
    - "the inductive constraint programming loop"

Connected through hyperedge HE-8 (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"
    - "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.AI(Artificial Intelligence)