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: "efficient non parametric bayesian hawkes processes"
  Abstract: In this paper, we develop a non-parametric Bayesian estimation of Hawkes process kernel functions. Our method is based on the cluster representation of Hawkes processes. We sample random branching structures, and thus split the Hawkes process into clusters of Poisson processes, where the intensity function of each of these processes is the nonparametric triggering kernel of the Hawkes process. We derive both a block Gibbs sampler and a maximum a posteriori estimator based on stochastic expectation maximization. On synthetic data, we show our method to be flexible and scalable, and on two largescale Twitter diffusion datasets, we show our method to outperform the parametric Hawkes model. We observe that the learned non-parametric kernel reflects the longevity of different content types. Code has been made publicly available.

Connected through hyperedge HE-1 (induced by source paper "quantile propagation for wasserstein approximate gaussian processes"):
  with members:
    - "variational inference for sparse gaussian process modulated hawkes process"
    - "improved training of wasserstein gans"
    - "expectation propagation for approximate bayesian inference"

Connected through hyperedge HE-2 (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"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "inferring parameters and structure of latent variable models by variational bayes"

Connected through hyperedge HE-3 (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"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "inferring parameters and structure of latent variable models by variational bayes"

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)