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: "data driven sequential monte carlo in probabilistic programming"
  Abstract: Most of Markov Chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) algorithms in existing probabilistic programming systems suboptimally use only model priors as proposal distributions. In this work, we describe an approach for training a discriminative model, namely a neural network, in order to approximate the optimal proposal by using posterior estimates from previous runs of inference. We show an example that incorporates a data-driven proposal for use in a non-parametric model in the Anglican probabilistic programming system. Our results show that data-driven proposals can significantly improve inference performance so that considerably fewer particles are necessary to perform a good posterior estimation.

Connected through hyperedge HE-1 (induced by source paper "multiverse causal reasoning using importance sampling in probabilistic programming"):
  with members:
    - "counterfactual off policy evaluation with gumbel max structural causal models"
    - "universal marginalizer for amortised inference and embedding of generative models"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "pyro deep universal probabilistic programming"
    - "local rule based explanations of black box decision systems"
    - "counterfactual multi agent policy gradients"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "using synthetic data to train neural networks is model based reasoning"

Connected through hyperedge HE-2 (induced by source paper "universal marginaliser for deep amortised inference for probabilistic programs"):
  with members:
    - "learning about an exponential amount of conditional distributions"
    - "universal marginalizer for amortised inference and embedding of generative models"
    - "pyro deep universal probabilistic programming"
    - "attention is all you need"
    - "using synthetic data to train neural networks is model based reasoning"
    - "inference compilation and universal probabilistic programming"
    - "deep amortized inference for probabilistic programs"
    - "made masked autoencoder for distribution estimation"

Connected through hyperedge HE-3 (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"
    - "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"
    - "structured priors for structure learning"

Connected through hyperedge HE-4 (induced by source paper "design and implementation of probabilistic programming language anglican"):
  with members:
    - "semantics for probabilistic programming higher order functions continuous distributions and soft constraints"
    - "a lambda calculus foundation for universal probabilistic programming"
    - "output sensitive adaptive metropolis hastings for probabilistic programs"
    - "a compilation target for probabilistic programming languages"
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "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)