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: "inducing probabilistic programs by bayesian program merging"
  Abstract: This report outlines an approach to learning generative models from data. We express models as probabilistic programs, which allows us to capture abstract patterns within the examples. By choosing our language for programs to be an extension of the algebraic data type of the examples, we can begin with a program that generates all and only the examples. We then introduce greater abstraction, and hence generalization, incrementally to the extent that it improves the posterior probability of the examples given the program. Motivated by previous approaches to model merging and program induction, we search for such explanatory abstractions using program transformations. We consider two types of transformation: Abstraction merges common subexpressions within a program into new functions (a form of anti-unification). Deargumentation simplifies functions by reducing the number of arguments. We demonstrate that this approach finds key patterns in the domain of nested lists, including parameterized sub-functions and stochastic recursion.

Connected through hyperedge HE-1 (induced by source paper "bayesian synthesis of probabilistic programs for automatic data modeling"):
  with members:
    - "probabilistic data analysis with probabilistic programming"
    - "deepcoder learning to write programs"
    - "crosscat a fully bayesian nonparametric method for analyzing heterogeneous high dimensional data"
    - "neural programmer interpreters"
    - "neural turing machines"
    - "learning probabilistic programs"
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "church a language for generative models"

Connected through hyperedge HE-2 (induced by source paper "inference over programs that make predictions"):
  with members:
    - "a universal marginalizer for amortized inference in generative models"
    - "inference compilation and universal probabilistic programming"
    - "data driven sequential monte carlo in probabilistic programming"
    - "a compilation target for probabilistic programming languages"
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "approximate bayesian image interpretation using generative probabilistic graphics programs"
    - "efficient estimation of word representations in vector space"
    - "church a language for generative models"

Connected through hyperedge HE-3 (induced by source paper "learning probabilistic programs"):
  with members:
    - "a compilation target for probabilistic programming languages"
    - "structured generative models of natural source code"
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "church a language for generative models"
    - "exploiting compositionality to explore a large space of model structures"

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)