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: "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.

Connected through hyperedge HE-1 (induced by source paper "a note on the quasiconvex jensen divergences and the quasiconvex bregman divergences derived thereof"):
  with members:
    - "on the jensen shannon symmetrization of distances relying on abstract means"
    - "beyond convexity stochastic quasi convex optimization"
    - "relative loss bounds for on line density estimation with the exponential family of distributions"
    - "the burbea rao and bhattacharyya centroids"

Connected through hyperedge HE-2 (induced by source paper "an elementary introduction to information geometry"):
  with members:
    - "guaranteed bounds on information theoretic measures of univariate mixtures using piecewise log sum exp inequalities"
    - "on the chi square and higher order chi distances for approximating f divergences"
    - "on conformal divergences and their population minimizers"
    - "cramer rao lower bound and information geometry"
    - "the burbea rao and bhattacharyya centroids"
    - "statistical exponential families a digest with flash cards"
    - "hyperbolic voronoi diagrams made easy"

Connected through hyperedge HE-3 (induced by source paper "clustering in hilbert simplex geometry"):
  with members:
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "an elementary introduction to information geometry"
    - "representation tradeoffs for hyperbolic embeddings"
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "fast 1 epsilon approximation of the lowner extremal matrices of high dimensional symmetric matrices"
    - "large margin nearest neighbor classification using curved mahalanobis distances"
    - "further heuristics for k means the merge and split heuristic and the k l means"
    - "no dimension independent core sets for containment under homothetics"

Connected through hyperedge HE-4 (induced by source paper "a generalization of the jensen divergence the chord gap divergence"):
  with members:
    - "a closed form expression for the sharma mittal entropy of exponential families"
    - "the burbea rao and bhattacharyya centroids"

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)