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: "recognition networks for approximate inference in bn20 networks"
  Abstract: We propose using recognition networks for approximate inference inBayesian networks (BNs). A recognition network is a multilayerperception (MLP) trained to predict posterior marginals given observedevidence in a particular BN. The input to the MLP is a vector of thestates of the evidential nodes. The activity of an output unit isinterpreted as a prediction of the posterior marginal of thecorresponding variable. The MLP is trained using samples generated fromthe corresponding BN.We evaluate a recognition network that was trained to do inference ina large Bayesian network, similar in structure and complexity to theQuick Medical Reference, Decision Theoretic (QMR-DT). Our networkis a binary, two-layer, noisy-OR network containing over 4000 potentially observable nodes and over 600 unobservable, hidden nodes. Inreal medical diagnosis, most observables are unavailable, and there isa complex and unknown bias that selects which ones are provided. Weincorporate a very basic type of selection bias in our network: a knownpreference that available observables are positive rather than negative.Even this simple bias has a significant effect on the posterior. We compare the performance of our recognition network tostate-of-the-art approximate inference algorithms on a large set oftest cases. In order to evaluate the effect of our simplistic modelof the selection bias, we evaluate algorithms using a variety ofincorrectly modeled observation biases. Recognition networks performwell using both correct and incorrect observation biases.

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 "the accuracy vs coverage trade off in patient facing diagnosis models"):
  with members:
    - "open set medical diagnosis"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "a comparative study of artificial intelligence and human doctors for the purpose of triage and diagnosis"
    - "learning from the experts from expert systems to machine learned diagnosis models"
    - "scalable and accurate deep learning for electronic health records"
    - "an empirical analysis of likelihood weighting simulation on a large multiply connected belief network"
    - "variational probabilistic inference and the qmr dt network"

Connected through hyperedge HE-4 (induced by source paper "amortized inference of variational bounds for learning noisy or"):
  with members:
    - "adam a method for stochastic optimization"
    - "neural variational inference and learning in belief networks"
    - "unsupervised learning of noisy or bayesian networks"
    - "variational probabilistic inference and the qmr dt network"
    - "mean field theory for sigmoid belief networks"

Connected through hyperedge HE-5 (induced by source paper "learning from the experts from expert systems to machine learned diagnosis models"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "scalable and accurate deep learning for electronic health records"
    - "an empirical analysis of likelihood weighting simulation on a large multiply connected belief network"

Connected through hyperedge HE-6 (induced by source paper "a universal marginalizer for amortized inference in generative models"):
  with members:
    - "using synthetic data to train neural networks is model based reasoning"
    - "made masked autoencoder for distribution estimation"
    - "adam a method for stochastic optimization"
    - "ais bn an adaptive importance sampling algorithm for evidential reasoning in large bayesian networks"

Connected through hyperedge HE-7 (induced by source paper "probabilistic programs for inferring the goals of autonomous agents"):
  with members:
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "church a language for generative models"

Connected through hyperedge HE-8 (induced by source paper "encapsulating models and approximate inference programs in probabilistic modules"):
  with members:
    - "probabilistic data analysis with probabilistic programming"
    - "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"
    - "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)