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: "generalised pinsker inequalities"
  Abstract: We generalise the classical Pinsker inequality which relates variational divergence to Kullback-Liebler divergence in two ways: we consider arbitrary f-divergences in place of KL divergence, and we assume knowledge of a sequence of values of generalised variational divergences. We then develop a best possible inequality for this doubly generalised situation. Specialising our result to the classical case provides a new and tight explicit bound relating KL to variational divergence (solving a problem posed by Vajda some 40 years ago). The solution relies on exploiting a connection between divergences and the Bayes risk of a learning problem via an integral representation.

Connected through hyperedge HE-1 (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"
    - "generalized bhattacharyya and chernoff upper bounds on bayes error using quasi arithmetic means"
    - "statistical exponential families a digest with flash cards"

Connected through hyperedge HE-2 (induced by source paper "on w mixtures finite convex combinations of prescribed component distributions"):
  with members:
    - "ensemble estimation of distributional functionals via k nearest neighbors"
    - "estimating mixture entropy with pairwise distances"
    - "on the chi square and higher order chi distances for approximating f divergences"
    - "multivariate f divergence estimation with confidence"
    - "k mle a fast algorithm for learning statistical mixture models"
    - "chernoff information of exponential families"
    - "on renyi and tsallis entropies and divergences for exponential families"
    - "the burbea rao and bhattacharyya centroids"

Connected through hyperedge HE-3 (induced by source paper "uniform generalization concentration and adaptive learning"):
  with members:
    - "typicality based stability and privacy"
    - "adaptive learning with robust generalization guarantees"
    - "train faster generalize better stability of stochastic gradient descent"
    - "algorithmic stability for adaptive data analysis"
    - "preserving statistical validity in adaptive data analysis"

Connected through hyperedge HE-4 (induced by source paper "and compression of np complete problems streamlined proof and minor observations"):
  with members:
    - "point line cover the easy kernel is essentially tight"
    - "kernelization lower bounds by cross composition"
    - "kernelization of packing problems"
    - "co nondeterminism in compositions a kernelization lower bound for a ramsey type problem"

Connected through hyperedge HE-5 (induced by source paper "minimum kl divergence on complements of l_1 balls"):
  with members:
    - "renyi divergence and kullback leibler divergence"
    - "on pinsker s and vajda s type inequalities for csiszar s f divergences"
    - "on pairs of f divergences and their joint range"

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)