human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "on the jensen shannon symmetrization of distances relying on abstract means"):
  Abstract: The Jensen–Shannon divergence is a renowned bounded symmetrization of the unbounded Kullback–Leibler divergence which measures the total Kullback–Leibler divergence to the average mixture distribution. However, the Jensen–Shannon divergence between Gaussian distributions is not available in closed form. To bypass this problem, we present a generalization of the Jensen–Shannon (JS) divergence using abstract means which yields closed-form expressions when the mean is chosen according to the parametric family of distributions. More generally, we define the JS-symmetrizations of any distance using parameter mixtures derived from abstract means. In particular, we first show that the geometric mean is well-suited for exponential families, and report two closed-form formula for (i) the geometric Jensen–Shannon divergence between probability densities of the same exponential family; and (ii) the geometric JS-symmetrization of the reverse Kullback–Leibler divergence between probability densities of the same exponential family. As a second illustrating example, we show that the harmonic mean is well-suited for the scale Cauchy distributions, and report a closed-form formula for the harmonic Jensen–Shannon divergence between scale Cauchy distributions. Applications to clustering with respect to these novel Jensen–Shannon divergences are touched upon.

Includes members:
  Member "on power chi expansions of f divergences":
    also connected through other hyperedges:
      (none)
  Member "an elementary introduction to information geometry":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the alpha divergences associated with a pair of strictly comparable quasi arithmetic means")
  Member "monte carlo information geometry the dually flat case":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on a generalization of the jensen shannon divergence")
  Member "guaranteed bounds on information theoretic measures of univariate mixtures using piecewise log sum exp inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a generalization of the α divergences based on comparable and distinct weighted means")
  Member "optimal interval clustering application to bregman clustering and statistical mixture learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "monte carlo information geometry the dually flat case")
  Member "on the chi square and higher order chi distances for approximating f divergences":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "histonet predicting size histograms of object instances")
  Member "generalized bhattacharyya and chernoff upper bounds on bayes error using quasi arithmetic means":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the alpha divergences associated with a pair of strictly comparable quasi arithmetic means")
  Member "bootstrap methods for the empirical study of decision making and information flows in social systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dialectometric analysis of language variation in twitter")

Question: Please tell me which class the source paper 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)