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 conformal divergences and their population minimizers"
  Abstract: Total Bregman divergences are a recent tweak of ordinary Bregman divergences originally motivated by applications that required invariance by rotations. They have displayed superior results compared to ordinary Bregman divergences on several clustering, computer vision, medical imaging and machine learning tasks. These preliminary results raise two important problems : First, report a complete characterization of the left and right population minimizers for this class of total Bregman divergences. Second, characterize a principled superset of total and ordinary Bregman divergences with good clustering properties, from which one could tailor the choice of a divergence to a particular application. In this paper, we provide and study one such superset with interesting geometric features, that we call conformal divergences, and focus on their left and right population minimizers. Our results are obtained in a recently coined $(u, v)$-geometric structure that is a generalization of the dually flat affine connections in information geometry. We characterize both analytically and geometrically the population minimizers. We prove that conformal divergences (resp. total Bregman divergences) are essentially exhaustive for their left (resp. right) population minimizers. We further report new results and extend previous results on the robustness to outliers of the left and right population minimizers, and discuss the role of the $(u, v)$-geometric structure in clustering. Additional results are also given.

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"
    - "the burbea rao and bhattacharyya centroids"

Connected through hyperedge HE-2 (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"
    - "the burbea rao and bhattacharyya centroids"

Connected through hyperedge HE-3 (induced by source paper "bregman divergence bounds and universality properties of the logarithmic loss"):
  with members:
    - "on f divergences integral representations local behavior and inequalities"
    - "on the universality of the logistic loss function"
    - "universality of logarithmic loss in lossy compression"
    - "opening the black box of deep neural networks via information"
    - "f divergence inequalities"
    - "generalized independent component analysis over finite alphabets"
    - "justification of logarithmic loss via the benefit of side information"
    - "deep learning and the information bottleneck principle"

Connected through hyperedge HE-4 (induced by source paper "an elementary introduction to information geometry"):
  with members:
    - "on holder projective divergences"
    - "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"
    - "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-5 (induced by source paper "generalizing jensen and bregman divergences with comparative convexity and the statistical bhattacharyya distances with comparable means"):
  with members:
    - "generalized bhattacharyya and chernoff upper bounds on bayes error using quasi arithmetic means"
    - "the burbea rao and bhattacharyya centroids"

Connected through hyperedge HE-6 (induced by source paper "k variates more pluses in the k means"):
  with members:
    - "optimal interval clustering application to bregman clustering and statistical mixture learning"
    - "an algorithm for online k means clustering"
    - "the noisy power method a meta algorithm with applications"
    - "distributed k means and k median clustering on general topologies"
    - "calibrating data to sensitivity in private data analysis"
    - "scalable k means"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-7 (induced by source paper "a scaled bregman theorem with applications"):
  with members:
    - "small variance nonparametric clustering on the hypersphere"
    - "projection free online learning"

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)