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: "nonparametric bayesian logic"
  Abstract: The Bayesian Logic (BLOG) language was recently developed for defining first-order probability models over worlds with unknown numbers of objects. It handles important problems in AI, including data association and population estimation. This paper extends BLOG by adopting generative processes over function spaces - known as nonparametrics in the Bayesian literature. We introduce syntax for reasoning about arbitrary collections of objects, and their properties, in an intuitive manner. By exploiting exchangeability, distributions over unknown objects and their attributes are cast as Dirichlet processes, which resolve difficulties in model selection and inference caused by varying numbers of objects. We demonstrate these concepts with application to citation matching.

Connected through hyperedge HE-1 (induced by source paper "on open universe causal reasoning"):
  with members:
    - "pyro deep universal probabilistic programming"
    - "on the conditional logic of simulation models"
    - "building machines that learn and think like people"
    - "axiomatizing causal reasoning"
    - "learning the structure of dynamic probabilistic networks"
    - "church a language for generative models"
    - "towards common sense reasoning via conditional simulation legacies of turing in artificial intelligence"
    - "probabilistic theorem proving"

Connected through hyperedge HE-2 (induced by source paper "herded gibbs sampling"):
  with members:
    - "general purpose mcmc inference over relational structures"
    - "church a language for generative models"
    - "herding dynamic weights for partially observed random field models"

Connected through hyperedge HE-3 (induced by source paper "lifted relational variational inference"):
  with members:
    - "estimating continuous distributions in bayesian classifiers"
    - "spook a system for probabilistic object oriented knowledge representation"
    - "lifted inference for relational continuous models"
    - "extended lifted inference with joint formulas"

Connected through hyperedge HE-4 (induced by source paper "infinite hidden relational models"):
  with members:
    - "the author topic model for authors and documents"

Connected through hyperedge HE-5 (induced by source paper "a bayesian model for supervised clustering with the dirichlet process prior"):
  with members:
    - "objective criteria for the evaluation of clustering methods"

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)