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 holder projective divergences"):
  Abstract: We describe a framework to build distances by measuring the tightness of inequalities and introduce the notion of proper statistical divergences and improper pseudo-divergences. We then consider the Holder ordinary and reverse inequalities and present two novel classes of Holder divergences and pseudo-divergences that both encapsulate the special case of the Cauchy–Schwarz divergence. We report closed-form formulas for those statistical dissimilarities when considering distributions belonging to the same exponential family provided that the natural parameter space is a cone (e.g., multivariate Gaussians) or affine (e.g., categorical distributions). Those new classes of Holder distances are invariant to rescaling and thus do not require distributions to be normalized. Finally, we show how to compute statistical Holder centroids with respect to those divergences and carry out center-based clustering toy experiments on a set of Gaussian distributions which demonstrate empirically that symmetrized Holder divergences outperform the symmetric Cauchy–Schwarz divergence.

Includes members:
  Member "guaranteed bounds on information theoretic measures of univariate mixtures using piecewise log sum exp inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a generalization of the α divergences based on comparable and distinct weighted means")
  Member "on the chi square and higher order chi distances for approximating f divergences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the jensen shannon symmetrization of distances relying on abstract means")
  Member "a closed form expression for the sharma mittal entropy of exponential families":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "q vae for disentangled representation learning and latent dynamical systems")
  Member "the burbea rao and bhattacharyya centroids":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the alpha divergences associated with a pair of strictly comparable quasi arithmetic means")
  Member "statistical exponential families a digest with flash cards":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the jensen shannon symmetrization of distances relying on abstract 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)