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: "probabilistic data analysis with probabilistic programming"
  Abstract: Probabilistic techniques are central to data analysis, but different approaches can be difficult to apply, combine, and compare. This paper introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and compose a broad class of probabilistic data analysis techniques. Examples include hierarchical Bayesian models, multivariate kernel methods, discriminative machine learning, clustering algorithms, dimensionality reduction, and arbitrary probabilistic programs. We also demonstrate the integration of CGPMs into BayesDB, a probabilistic programming platform that can express data analysis tasks using a modeling language and a structured query language. The practical value is illustrated in two ways. First, CGPMs are used in an analysis that identifies satellite data records which probably violate Kepler's Third Law, by composing causal probabilistic programs with non-parametric Bayes in under 50 lines of probabilistic code. Second, for several representative data analysis tasks, we report on lines of code and accuracy measurements of various CGPMs, plus comparisons with standard baseline solutions from Python and MATLAB libraries.

Connected through hyperedge HE-1 (induced by source paper "optimal approximate sampling from discrete probability distributions"):
  with members:
    - "bayesian synthesis of probabilistic programs for automatic data modeling"
    - "coalgebraic tools for randomness conserving protocols"
    - "sampling with arbitrary precision"
    - "building fast bayesian computing machines out of intentionally stochastic digital parts"
    - "optimal discrete uniform generation from coin flips and applications"
    - "efficient generation e close to g n p and generalizations"

Connected through hyperedge HE-2 (induced by source paper "bayesian synthesis of probabilistic programs for automatic data modeling"):
  with members:
    - "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"
    - "structured priors for structure learning"

Connected through hyperedge HE-3 (induced by source paper "probabilistic search for structured data via probabilistic programming and nonparametric bayes"):
  with members:
    - "bayesdb a probabilistic programming system for querying the probable implications of data"

Connected through hyperedge HE-4 (induced by source paper "encapsulating models and approximate inference programs in probabilistic modules"):
  with members:
    - "measuring the non asymptotic convergence of sequential monte carlo samplers using probabilistic programming"
    - "quantifying the probable approximation error of probabilistic inference programs"
    - "bayesdb a probabilistic programming system for querying the probable implications of data"
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "recognition networks for approximate inference in bn20 networks"
    - "church a language for generative models"

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)