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: "gibbs sampling in open universe stochastic languages"
  Abstract: Languages for open-universe probabilistic models (OUPMs) can represent situations with an unknown number of objects and iden- tity uncertainty. While such cases arise in a wide range of important real-world appli- cations, existing general purpose inference methods for OUPMs are far less efficient than those available for more restricted lan- guages and model classes. This paper goes some way to remedying this deficit by in- troducing, and proving correct, a generaliza- tion of Gibbs sampling to partial worlds with possibly varying model structure. Our ap- proach draws on and extends previous generic OUPM inference methods, as well as aux- iliary variable samplers for nonparametric mixture models. It has been implemented for BLOG, a well-known OUPM language. Combined with compile-time optimizations, the resulting algorithm yields very substan- tial speedups over existing methods on sev- eral test cases, and substantially improves the practicality of OUPM languages generally.

Connected through hyperedge HE-1 (induced by source paper "compositional inference metaprogramming with convergence guarantees"):
  with members:
    - "a convenient category for higher order probability theory"
    - "denotational validation of higher order bayesian inference"
    - "swift compiled inference for probabilistic programming languages"
    - "a lambda calculus foundation for universal probabilistic programming"
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "church a language for generative models"
    - "probabilistic operational semantics for the lambda calculus"

Connected through hyperedge HE-2 (induced by source paper "swift compiled inference for probabilistic programming languages"):
  with members:
    - "simpl a dsl for automatic specialization of inference algorithms"
    - "c3 lightweight incrementalized mcmc for probabilistic programs using continuations and callsite caching"
    - "approximate bayesian image interpretation using generative probabilistic graphics programs"
    - "general purpose mcmc inference over relational structures"
    - "church a language for generative models"

Connected through hyperedge HE-3 (induced by source paper "venture a higher order probabilistic programming platform with programmable inference"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "approximate bayesian image interpretation using generative probabilistic graphics programs"
    - "church a language for generative models"
    - "structured priors for structure learning"
    - "exploiting compositionality to explore a large space of model structures"

Connected through hyperedge HE-4 (induced by source paper "probabilistic programming concepts"):
  with members:
    - "relational bayesian networks"
    - "church a language for generative models"
    - "clp bn constraint logic programming for probabilistic knowledge"
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"
    - "probabilistic reasoning with answer sets"

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)