human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "encapsulating models and approximate inference programs in probabilistic modules"):
  Abstract: This paper introduces the probabilistic module interface, which allows encapsulation of complex probabilistic models with latent variables alongside custom stochastic approximate inference machinery, and provides a platform-agnostic abstraction barrier separating the model internals from the host probabilistic inference system. The interface can be seen as a stochastic generalization of a standard simulation and density interface for probabilistic primitives. We show that sound approximate inference algorithms can be constructed for networks of probabilistic modules, and we demonstrate that the interface can be implemented using learned stochastic inference networks and MCMC and SMC approximate inference programs.

Includes members:
  Member "probabilistic data analysis with probabilistic programming":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimal approximate sampling from discrete probability distributions")
  Member "measuring the non asymptotic convergence of sequential monte carlo samplers using probabilistic programming":
    also connected through other hyperedges:
      (none)
  Member "quantifying the probable approximation error of probabilistic inference programs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "measuring the non asymptotic convergence of sequential monte carlo samplers using probabilistic programming")
  Member "bayesdb a probabilistic programming system for querying the probable implications of data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "probabilistic programming with densities in slicstan efficient flexible and deterministic")
  Member "venture a higher order probabilistic programming platform with programmable inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stochastically differentiable probabilistic programs")
  Member "recognition networks for approximate inference in bn20 networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multiverse causal reasoning using importance sampling in probabilistic programming")
  Member "church a language for generative models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "semiring programming a declarative framework for generalized sum product problems")

Question: Please tell me which class the source paper 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)