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: "lower bounds on information divergence"
  Abstract: In this paper we establish lower bounds on informa- tion divergence from a distribution to certain important classes of distributions as Gaussian, exponential, Gamma, Poisson, ge- ometric, and binomial. These lower bounds are tight and for several convergence theorems where a rate of convergence can be computed, this rate is determined by the lower bounds proved in this paper. General techniques for getting lower bounds in terms of moments are developed. I. I NTRODUCTION AND NOTATIONS In 2004, O. Johnson and A. Barron have proved (1) that the rate of convergence in the information theoretic Central Limit Theorem is upper bounded by c=n under suitable conditions. P. Harremoes extended this work in (2) based on a maximum entropy approach. Similar results have been obtained for the convergence of binomial distributions to Poisson distributions. Finally the rate of convergence of convolutions of distribu tions on the unit circle toward the uniform distribution can be bounded. In each of these cases lower bounds on information divergence in terms of moments of orthogonal polynomials or trigonometric functions give lower bounds on the rate of convergence. In this paper, we provide more lower bounds on information divergence using mainly orthogonal polynomials and the related exponential families. We will identify x! with ( x + 1) even when x is not an integer. Similarly the generalized binomial coefficient x � equals x(x − 1)���(x − n + 1)=n! when x is not an integer. We useas short for 2� .

Connected through hyperedge HE-1 (induced by source paper "improved lower bounds on the total variation distance for the poisson approximation"):
  with members:
    - "sharp bounds on the entropy of the poisson law and related quantities"

Connected through hyperedge HE-2 (induced by source paper "an information theoretic perspective of the poisson approximation via the chen stein method"):
  with members:
    - "monotonicity thinning and discrete versions of the entropy power inequality"
    - "sharp bounds on the entropy of the poisson law and related quantities"
    - "thinning entropy and the law of thin numbers"
    - "monotonic convergence in an information theoretic law of small numbers"

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)