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 "on w mixtures finite convex combinations of prescribed component distributions"):
  Abstract: We consider the space of $w$-mixtures that is the set of finite statistical mixtures sharing the same prescribed component distributions. The geometry induced by the Kullback-Leibler (KL) divergence on this family of $w$-mixtures is a dually flat space in information geometry called the mixture family manifold. It follows that the KL divergence between two $w$-mixtures is equivalent to a Bregman Divergence (BD) defined for the negative Shannon entropy generator. Thus the KL divergence between two Gaussian Mixture Models (GMMs) sharing the same components is (theoretically) a Bregman divergence. This KL-BD equivalence implies that we can perform optimal KL-averaging aggregation of $w$-mixtures without information loss. More generally, we prove that the skew Jensen-Shannon divergence between $w$-mixtures is equivalent to a skew Jensen divergence on their parameters. Finally, we state several divergence identity and inequalities relating $w$-mixtures.

Includes members:
  Member "ensemble estimation of distributional functionals via k nearest neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ensemble estimation of generalized mutual information with applications to genomics")
  Member "estimating mixture entropy with pairwise distances":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "understanding and enhancing mixed sample data augmentation")
  Member "on the chi square and higher order chi distances for approximating f divergences":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the jensen shannon symmetrization of distances relying on abstract means")
  Member "multivariate f divergence estimation with confidence":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "geometric estimation of multivariate dependency")
  Member "k mle a fast algorithm for learning statistical mixture models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimal interval clustering application to bregman clustering and statistical mixture learning")
  Member "chernoff information of exponential families":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "information centric grant free access for iot fog networks edge vs cloud detection and learning")
  Member "on renyi and tsallis entropies and divergences for exponential families":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the chain rule optimal transport distance")
  Member "the burbea rao and bhattacharyya centroids":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the alpha divergences associated with a pair of strictly comparable quasi arithmetic means")

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)