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 "generative datalog with continuous distributions"):
  Abstract: Arguing for the need to combine declarative and probabilistic programming, B\'ar\'any et al. (TODS 2017) recently introduced a probabilistic extension of Datalog as a "purely declarative probabilistic programming language." We revisit this language and propose a more principled approach towards defining its semantics. It is based on standard notions from probability theory known as stochastic kernels and Markov processes. This allows us to extend the semantics to continuous probability distributions, thereby settling an open problem posed by B\'ar\'any et al. We show that our semantics is fairly robust, allowing both parallel execution and arbitrary chase orders when evaluating a program. We cast our semantics in the framework of infinite probabilistic databases (Grohe and Lindner, ICDT 2020), and we show that the semantics remains meaningful even when the input of a probabilistic Datalog program is an arbitrary probabilistic database.

Includes members:
  Member "infinite probabilistic databases":
    also connected through other hyperedges:
      (none)
  Member "pyro deep universal probabilistic programming":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")
  Member "discrete continuous mixtures in probabilistic programming generalized semantics and inference algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards verified stochastic variational inference for probabilistic programs")
  Member "probabilistic databases with an infinite open world assumption":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "infinite probabilistic databases")
  Member "measure transformer semantics for bayesian machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "formal verification of higher order probabilistic programs reasoning about approximation convergence bayesian inference and optimization")
  Member "markov logic in infinite domains":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "abstracting probabilistic models a logical perspective")

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.DB(Databases)