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: "generalized bhattacharyya and chernoff upper bounds on bayes error using quasi arithmetic means"
  Abstract: Bayesian classification labels observations based on given prior information, namely class-a priori and class-conditional probabilities. Bayes’ risk is the minimum expected classification cost that is achieved by the Bayes’ test, the optimal decision rule. When no cost incurs for correct classification and unit cost is charged for misclassification, Bayes’ test reduces to the maximum a posteriori decision rule, and Bayes risk simplifies to Bayes’ error, the probability of error. Since calculating this probability of error is often intractable, several techniques have been devised to bound it with closed-form formula, introducing thereby measures of similarity and divergence between distributions like the Bhattacharyya coefficient and its associated Bhattacharyya distance. The Bhattacharyya upper bound can further be tightened using the Chernoff information that relies on the notion of best error exponent. In this paper, we first express Bayes’ risk using the total variation distance on scaled distributions. We then elucidate and extend the Bhattacharyya and the Chernoff upper bound mechanisms using generalized weighted means. We provide as a byproduct novel notions of statistical divergences and affinity coefficients. We illustrate our technique by deriving new upper bounds for the univariate Cauchy and the multivariate t-distributions, and show experimentally that those bounds are not too distant to the computationally intractable Bayes’ error.

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"
    - "on conformal divergences and their population minimizers"
    - "the burbea rao and bhattacharyya centroids"

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "on the chain rule optimal transport distance"):
  with members:
    - "guaranteed deterministic bounds on the total variation distance between univariate mixtures"
    - "monte carlo information geometry the dually flat case"
    - "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"
    - "a closed form expression for the sharma mittal entropy of exponential families"
    - "on renyi and tsallis entropies and divergences for exponential families"

Connected through hyperedge HE-4 (induced by source paper "guaranteed deterministic bounds on the total variation distance between univariate mixtures"):
  with members:
    - "guaranteed bounds on information theoretic measures of univariate mixtures using piecewise log sum exp inequalities"
    - "f divergence inequalities"
    - "on reverse pinsker inequalities"
    - "on the chi square and higher order chi distances for approximating f divergences"
    - "generalised pinsker inequalities"
    - "statistical exponential families a digest with flash cards"

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:
    - "on conformal divergences and their population minimizers"
    - "the burbea rao and bhattacharyya centroids"

Connected through hyperedge HE-6 (induced by source paper "estimating mixture entropy with pairwise distances"):
  with members:
    - "f divergence inequalities"
    - "renyi divergence and kullback leibler divergence"

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.CV(Computer Vision and Pattern Recognition)