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 entropy of sums of bernoulli random variables via the chen stein method"):
  Abstract: This paper 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. The derivation of these bounds combines elements of information theory with the Chen-Stein method for Poisson approximation. The resulting bounds are easy to compute, and their applicability is exemplified. This conference paper presents in part the first half of the paper entitled “An information-theoretic perspective of the Poisson approximation via the Chen-Stein method” (see: http://arxiv.org/abs/1206.6811). A generalization of the bounds that considers the accuracy of the Poisson approximation for the entropy of a sum of non-negative, integer-valued and bounded random variables is introduced in the full paper. It also derives lower bounds on the total variation distance, relative entropy and other measures that are not considered in this conference paper.

Includes members:
  Member "an information theoretic perspective of the poisson approximation via the chen stein method":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tight bounds for symmetric divergence measures and a refined bound for lossless source coding")
  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)