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 "from high level inference algorithms to efficient code"):
  Abstract: Probabilistic programming languages are valuable because they allow domain experts to express probabilistic models and inference algorithms without worrying about irrelevant details. However, for decades there remained an important and popular class of probabilistic inference algorithms whose efficient implementation required manual low-level coding that is tedious and error-prone. They are algorithms whose idiomatic expression requires random array variables that are latent or whose likelihood is conjugate. Although that is how practitioners communicate and compose these algorithms on paper, executing such expressions requires eliminating the latent variables and recognizing the conjugacy by symbolic mathematics. Moreover, matching the performance of handwritten code requires speeding up loops by more than a constant factor. #R##N#We show how probabilistic programs that directly and concisely express these desired inference algorithms can be compiled while maintaining efficiency. We introduce new transformations that turn high-level probabilistic programs with arrays into pure loop code. We then make great use of domain-specific invariants and norms to optimize the code, and to specialize and JIT-compile the code per execution. The resulting performance is competitive with manual implementations.

Includes members:
  Member "autoconj recognizing and exploiting conjugacy without a domain specific language":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "static analysis for probabilistic programs")
  Member "exact inference for relational graphical models with interpreted functions lifted probabilistic inference modulo theories":
    also connected through other hyperedges:
      (none)
  Member "swift compiled inference for probabilistic programming languages":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generative grading neural approximate parsing for automated student feedback")
  Member "probabilistic inference modulo theories":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scaling up hybrid probabilistic inference with logical and arithmetic constraints via message passing")
  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 "dynamic blocking and collapsing for gibbs sampling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meta learning mcmc proposals")
  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")
  Member "exploiting contextual independence in probabilistic inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "addmc exact weighted model counting with algebraic decision diagrams")

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.PL(Programming Languages)