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: "variational probabilistic inference and the qmr dt network"
  Abstract: We describe a variational approximation method for efficient inference in large-scale probabilistic models. Variational methods are deterministic procedures that provide approximations to marginal and conditional probabilities of interest. They provide alternatives to approximate inference methods based on stochastic sampling or search. We describe a variational approach to the problem of diagnostic inference in the "Quick Medical Reference" (QMR) network. The QMR network is a large-scale probabilistic graphical model built on statistical and expert knowledge. Exact probabilistic inference is infeasible in this model for all but a small set of cases. We evaluate our variational inference algorithm on a large set of diagnostic test cases, comparing the algorithm to a state-of-the-art stochastic sampling method.

Connected through hyperedge HE-1 (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"
    - "recognition networks for approximate inference in bn20 networks"

Connected through hyperedge HE-2 (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"
    - "recognition networks for approximate inference in bn20 networks"
    - "mean field theory for sigmoid belief networks"

Connected through hyperedge HE-3 (induced by source paper "universal marginalizer for amortised inference and embedding of generative models"):
  with members:
    - "deep amortized inference for probabilistic programs"
    - "adam a method for stochastic optimization"
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "loopy belief propagation for approximate inference an empirical study"

Connected through hyperedge HE-4 (induced by source paper "survey on models and techniques for root cause analysis"):
  with members:
    - "learning possibilistic logic theories from default rules"
    - "learning tractable probabilistic models for fault localization"
    - "using abduction in markov logic networks for root cause analysis"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "automated reasoning using possibilistic logic semantics belief revision and variable certainty weights"
    - "loopy belief propagation for approximate inference an empirical study"
    - "a differential approach to inference in bayesian networks"
    - "evaluating computational models of explanation using human judgments"

Connected through hyperedge HE-5 (induced by source paper "variational hybridization and transformation for large inaccurate noisy or networks"):
  with members:
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "addressing the rare word problem in neural machine translation"
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "loopy belief propagation for approximate inference an empirical study"
    - "unsupervised learning of noisy or bayesian networks"
    - "formula based probabilistic inference"
    - "structured priors for structure learning"

Connected through hyperedge HE-6 (induced by source paper "subsumptive reflection in snomed ct a large description logic based terminology for diagnosis"):
  with members:
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "a variational approximation for bayesian networks with discrete and continuous latent variables"):
  with members:
    - "approximations in bayesian belief universe for knowledge based systems"
    - "nonuniform dynamic discretization in hybrid networks"
    - "inference using message propagation and topology transformation in vector gaussian continuous networks"
    - "optimal junction trees"
    - "simulation approaches to general probabilistic inference on belief networks"
    - "global conditioning for probabilistic inference in belief networks"
    - "a sufficiently fast algorithm for finding close to optimal junction trees"

Connected through hyperedge HE-8 (induced by source paper "unsupervised learning of noisy or bayesian networks"):
  with members:
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "exact inference of hidden structure from sample data in noisy or networks"
    - "a method of moments for mixture models and hidden markov models"
    - "a spectral algorithm for latent dirichlet allocation"
    - "learning nonsingular phylogenies and hidden markov 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)