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: "inferring parameters and structure of latent variable models by variational bayes"
  Abstract: Current methods for learning graphical models with latent variables and a fixed structure estimate optimal values for the model parameters. Whereas this approach usually produces overfitting and suboptimal generalization performance, carrying out the Bayesian program of computing the full posterior distributions over the parameters remains a difficult problem. Moreover, learning the structure of models with latent variables, for which the Bayesian approach is crucial, is yet a harder problem. In this paper I present the Variational Bayes framework, which provides a solution to these problems. This approach approximates full posterior distributions over model parameters and structures, as well as latent variables, in an analytical manner without resorting to sampling methods. Unlike in the Laplace approximation, these posteriors are generally non-Gaussian and no Hessian needs to be computed. The resulting algorithm generalizes the standard Expectation Maximization algorithm, and its convergence is guaranteed. I demonstrate that this algorithm can be applied to a large class of models in several domains, including unsupervised clustering and blind source separation.

Connected through hyperedge HE-1 (induced by source paper "variational inference for sparse gaussian process modulated hawkes process"):
  with members:
    - "sir hawkes linking epidemic models and hawkes processes to model diffusions in finite populations"
    - "efficient non parametric bayesian hawkes processes"
    - "seismic a self exciting point process model for predicting tweet popularity"

Connected through hyperedge HE-2 (induced by source paper "sparse gaussian process modulated hawkes process"):
  with members:
    - "sir hawkes linking epidemic models and hawkes processes to model diffusions in finite populations"
    - "efficient non parametric bayesian hawkes processes"
    - "seismic a self exciting point process model for predicting tweet popularity"

Connected through hyperedge HE-3 (induced by source paper "maximum likelihood estimation of a finite mixture of logistic regression models in a continuous data stream"):
  with members:
    - "hierarchical exploration for accelerating contextual bandits"
    - "online learning stability and stochastic gradient descent"

Connected through hyperedge HE-4 (induced by source paper "rebuilding factorized information criterion asymptotically accurate marginal likelihood"):
  with members:
    - "on smoothing and inference for topic models"
    - "factorized asymptotic bayesian hidden markov models"
    - "a widely applicable bayesian information criterion"

Connected through hyperedge HE-5 (induced by source paper "a survey on latent tree models and applications"):
  with members:
    - "asymptotic model selection for directed networks with hidden variables"
    - "learning the dimensionality of hidden variables"
    - "spectral methods for learning multivariate latent tree structure"
    - "learning high dimensional markov forest distributions analysis of error rates"
    - "a spectral algorithm for learning hidden markov models"
    - "latent tree models and approximate inference in bayesian networks"
    - "learning nonsingular phylogenies and hidden markov models"
    - "effective dimensions of hierarchical latent class models"

Connected through hyperedge HE-6 (induced by source paper "quantum annealing for variational bayes inference"):
  with members:
    - "discriminative learning via semidefinite probabilistic 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.LG(Machine Learning)