human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "quantile propagation for wasserstein approximate gaussian processes"):
  Abstract: In this work, we develop a new approximation method to solve the analytically intractable Bayesian inference for Gaussian process models with factorizable Gaussian likelihoods and single-output latent functions. Our method -- dubbed QP -- is similar to the expectation propagation (EP), however it minimizes the $L^2$ Wasserstein distance instead of the Kullback-Leibler (KL) divergence. We consider the specific case in which the non-Gaussian likelihood is approximated by the Gaussian likelihood. We show that QP has the following properties: (1) QP matches quantile functions rather than moments in EP; (2) QP and EP have the same local update for the mean of the approximate Gaussian likelihood; (3) the local variance estimate for the approximate likelihood is smaller for QP than for EP's, addressing EP's over-estimation of the variance; (4) the optimal approximate Gaussian likelihood enjoys a univariate parameterization, reducing memory consumption and computation time. Furthermore, we provide a unified interpretations of EP and QP -- both are coordinate descent algorithms of a KL and an $L^2$ Wasserstein global objective function respectively, under the same assumptions. In the performed experiments, we employ eight real world datasets and we show that QP outperforms EP for the task of Gaussian process binary classification.

Includes members:
  Member "variational inference for sparse gaussian process modulated hawkes process":
    also connected through other hyperedges:
      (none)
  Member "efficient non parametric bayesian hawkes processes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "variational inference for sparse gaussian process modulated hawkes process")
  Member "improved training of wasserstein gans":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "expectation propagation for approximate bayesian inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bayesian deep learning and a probabilistic perspective of generalization")

Question: Please tell me which class the source paper 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)