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 "amortized rejection sampling in universal probabilistic programming"):
  Abstract: Existing approaches to amortized inference in probabilistic programs with unbounded loops can produce estimators with infinite variance. An instance of this is importance sampling inference in programs that explicitly include rejection sampling as part of the user-programmed generative procedure. In this paper we develop a new and efficient amortized importance sampling estimator. We prove finite variance of our estimator and empirically demonstrate our method's correctness and efficiency compared to existing alternatives on generative programs containing rejection sampling loops and discuss how to implement our method in a generic probabilistic programming framework.

Includes members:
  Member "etalumis bringing probabilistic programming to scientific simulators at scale":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximate inference in discrete distributions with monte carlo tree search and value functions")
  Member "efficient probabilistic inference in the quest for physics beyond the standard model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive divergence for rapid adversarial optimization")
  Member "denotational validation of higher order bayesian inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "correctness of sequential monte carlo inference for probabilistic programming languages")
  Member "deep amortized inference for probabilistic programs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta learning of sequential strategies")
  Member "a compilation target for probabilistic programming languages":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lazy object copy as a platform for population based probabilistic programming")
  Member "venture a higher order probabilistic programming platform with programmable inference":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stochastically differentiable probabilistic programs")
  Member "church a language for generative models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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.LG(Machine Learning)