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 "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.

Includes members:
  Member "lower bounds on information divergence":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improved lower bounds on the total variation distance for the poisson approximation")
  Member "monotonicity thinning and discrete versions of the entropy power inequality":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an entropy power inequality for discrete random variables")
  Member "sharp bounds on the entropy of the poisson law and related quantities":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "universal compression of power law distributions")
  Member "thinning entropy and the law of thin numbers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a de bruijn identity for discrete random variables")
  Member "monotonic convergence in an information theoretic law of small numbers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "forward and reverse entropy power inequalities in convex geometry")

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)