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: "an information theoretic perspective of the poisson approximation via the chen stein method"
  Abstract: The first part of this work considers the entropy of the sum of ( possibly dependent and non-identically distributed) Bernoulli random variables. Upper bounds on the error that follows from an approximation of this entropy by the entropy of a Poisson random variable with the same mean are derived via the Chen-Stein method. The second part of this work derives new lower bounds on the total variation distance and relative entropy between the distribution of the sum of independent Bernoulli random variables and the Poisson distribution. The starting point of the derivation of the new bounds in the second part of this work is an introduction of a new lower bound on the total variation distance, whose derivation generalizes and refines the anal ysis by Barbour and Hall (1984), based on the Chen-Stein method for the Poisson approximation. A new lower bound on the relative entropy between these two distributions is introd uced, and this lower bound is compared to a previously reported upper bound on the relative entropy by Kontoyiannis et al. (2005). The derivation of the new lower bound on the relative entropy follows from the new lower bound on the total variation distance, combined with a distribution-dependent refine ment of Pinsker’s inequality by Ordentlich and Weinberger (2005). Upper and lower bounds on the Bhattacharyya parameter, Chernoff information and Hellinger distance between the distribution of the sum of independent Bernoulli random variables and the Poisson distribution with the same mean are derived as well via some relations between these quantities with the total variation distance and the relative entropy. The analysis in this work combines elements of information theory with the Chen-Stein method for the Poisson approximation. The resulting bounds are easy to compute, and their applicability is exemplified.

Connected through hyperedge HE-1 (induced by source paper "tight bounds for symmetric divergence measures and a refined bound for lossless source coding"):
  with members:
    - "renyi divergence and kullback leibler divergence"
    - "improved lower bounds on the total variation distance for the poisson approximation"
    - "entropy bounds for discrete random variables via maximal coupling"
    - "on pinsker s and vajda s type inequalities for csiszar s f divergences"
    - "on pairs of f divergences and their joint range"

Connected through hyperedge HE-2 (induced by source paper "entropy bounds for discrete random variables via maximal coupling"):
  with members:
    - "improved lower bounds on the total variation distance for the poisson approximation"
    - "on the entropy of sums of bernoulli random variables via the chen stein method"
    - "sharp bounds on the entropy of the poisson law and related quantities"

Connected through hyperedge HE-3 (induced by source paper "on the entropy of sums of bernoulli random variables 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)