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: "the burbea rao and bhattacharyya centroids"
  Abstract: We study the centroid with respect to the class of information-theoretic Burbea-Rao divergences that generalize the celebrated Jensen-Shannon divergence by measuring the non-negative Jensen difference induced by a strictly convex and differentiable function. Although those Burbea-Rao divergences are symmetric by construction, they are not metric since they fail to satisfy the triangle inequality. We first explain how a particular symmetrization of Bregman divergences called Jensen-Bregman distances yields exactly those Burbea-Rao divergences. We then proceed by defining skew Burbea-Rao divergences, and show that skew Burbea-Rao divergences amount in limit cases to compute Bregman divergences. We then prove that Burbea-Rao centroids can be arbitrarily finely approximated by a generic iterative concave-convex optimization algorithm with guaranteed convergence property. In the second part of the paper, we consider the Bhattacharyya distance that is commonly used to measure overlapping degree of probability distributions. We show that Bhattacharyya distances on members of the same statistical exponential family amount to calculate a Burbea-Rao divergence in disguise. Thus we get an efficient algorithm for computing the Bhattacharyya centroid of a set of parametric distributions belonging to the same exponential families, improving over former specialized methods found in the literature that were limited to univariate or “diagonal” multivariate Gaussians. To illustrate the performance of our Bhattacharyya/Burbea-Rao centroid algorithm, we present experimental performance results for k-means and hierarchical clustering methods of Gaussian mixture models.

Connected through hyperedge HE-1 (induced by source paper "the alpha divergences associated with a pair of strictly comparable quasi arithmetic means"):
  with members:
    - "an elementary introduction to information geometry"
    - "generalized bhattacharyya and chernoff upper bounds on bayes error using quasi arithmetic means"
    - "on conformal divergences and their population minimizers"

Connected through hyperedge HE-2 (induced by source paper "minimization problems on strictly convex divergences"):
  with members:
    - "f divergence inequalities"
    - "renyi divergence and kullback leibler divergence"

Connected through hyperedge HE-3 (induced by source paper "a generalization of the α divergences based on comparable and distinct weighted means"):
  with members:
    - "guaranteed bounds on information theoretic measures of univariate mixtures using piecewise log sum exp inequalities"
    - "on conformal divergences and their population minimizers"

Connected through hyperedge HE-4 (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"
    - "monte carlo information geometry the dually flat case"
    - "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"

Connected through hyperedge HE-5 (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"
    - "monte carlo information geometry the dually flat case"
    - "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"
    - "a family of statistical symmetric divergences based on jensen s inequality"
    - "measures of distributional similarity"

Connected through hyperedge HE-6 (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"
    - "monte carlo information geometry the dually flat case"
    - "topological data analysis with bregman divergences"
    - "cramer rao lower bound and information geometry"
    - "statistical exponential families a digest with flash cards"

Connected through hyperedge HE-7 (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"
    - "on holder projective divergences"
    - "beyond convexity stochastic quasi convex optimization"
    - "relative loss bounds for on line density estimation with the exponential family of distributions"

Connected through hyperedge HE-8 (induced by source paper "metrics induced by jensen shannon and related divergences on positive definite matrices"):
  with members:
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"

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.IT(Information Theory)