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: "monte carlo information geometry the dually flat case"
  Abstract: Exponential families and mixture families are parametric probability models that can be geometrically studied as smooth statistical manifolds with respect to any statistical divergence like the Kullback-Leibler (KL) divergence or the Hellinger divergence. When equipping a statistical manifold with the KL divergence, the induced manifold structure is dually flat, and the KL divergence between distributions amounts to an equivalent Bregman divergence on their corresponding parameters. In practice, the corresponding Bregman generators of mixture/exponential families require to perform definite integral calculus that can either be too time-consuming (for exponentially large discrete support case) or even do not admit closed-form formula (for continuous support case). In these cases, the dually flat construction remains theoretical and cannot be used by information-geometric algorithms. To bypass this problem, we consider performing stochastic Monte Carlo (MC) estimation of those integral-based mixture/exponential family Bregman generators. We show that, under natural assumptions, these MC generators are almost surely Bregman generators. We define a series of dually flat information geometries, termed Monte Carlo Information Geometries, that increasingly-finely approximate the untractable geometry. The advantage of this MCIG is that it allows a practical use of the Bregman algorithmic toolbox on a wide range of probability distribution families. We demonstrate our approach with a clustering task on a mixture family manifold.

Connected through hyperedge HE-1 (induced by source paper "on the jensen shannon symmetrization of distances relying on abstract means"):
  with members:
    - "on power chi expansions of f divergences"
    - "an elementary introduction to information geometry"
    - "guaranteed bounds on information theoretic measures of univariate mixtures using piecewise log sum exp inequalities"
    - "optimal interval clustering application to bregman clustering and statistical mixture learning"
    - "on the chi square and higher order chi distances for approximating f divergences"
    - "generalized bhattacharyya and chernoff upper bounds on bayes error using quasi arithmetic means"
    - "bootstrap methods for the empirical study of decision making and information flows in social systems"
    - "jeffreys centroids a closed form expression for positive histograms and a guaranteed tight approximation for frequency histograms"

Connected through hyperedge HE-2 (induced by source paper "on a generalization of the jensen shannon divergence"):
  with members:
    - "on the jensen shannon symmetrization of distances relying on abstract means"
    - "on f divergences integral representations local behavior and inequalities"
    - "information measures the curious case of the binary alphabet"
    - "jeffreys centroids a closed form expression for positive histograms and a guaranteed tight approximation for frequency histograms"
    - "the burbea rao and bhattacharyya centroids"
    - "a family of statistical symmetric divergences based on jensen s inequality"
    - "measures of distributional similarity"

Connected through hyperedge HE-3 (induced by source paper "on geodesic triangles with right angles in a dually flat space"):
  with members:
    - "on a convergence property of a geometrical algorithm for statistical manifolds"
    - "an elementary introduction to information geometry"
    - "topological data analysis with bregman divergences"
    - "cramer rao lower bound and information geometry"
    - "the burbea rao and bhattacharyya centroids"
    - "statistical exponential families a digest with flash cards"

Connected through hyperedge HE-4 (induced by source paper "on the chain rule optimal transport distance"):
  with members:
    - "guaranteed deterministic bounds on the total variation distance between univariate mixtures"
    - "on w mixtures finite convex combinations of prescribed component distributions"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "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"
    - "generalized bhattacharyya and chernoff upper bounds on bayes error using quasi arithmetic means"
    - "a closed form expression for the sharma mittal entropy of exponential families"
    - "on renyi and tsallis entropies and divergences for exponential families"

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)