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: "sharp bounds on the entropy of the poisson law and related quantities"
  Abstract: One of the difficulties in calculating the capacity of certain Poisson channels is that H(?), the entropy of the Poisson distribution with mean ?, is not available in a simple form. In this paper, we derive upper and lower bounds for H(?) that are asymptotically tight and easy to compute. The derivation of such bounds involves only simple probabilistic and analytic tools. This complements the asymptotic expansions of Knessl (1998), Jacquet and Szpankowski (1999), and Flajolet (1999). The same method yields tight bounds on the relative entropy D(n, p) between a binomial and a Poisson, thus refining the work of Harremoe?s and Ruzankin (2004). Bounds on the entropy of the binomial also follow easily.

Connected through hyperedge HE-1 (induced by source paper "universal compression of power law distributions"):
  with members:
    - "about adaptive coding on countable alphabets max stable envelope classes"
    - "universal compression of envelope classes tight characterization via poisson sampling"
    - "universal source coding for monotonic and fast decaying monotonic distributions"

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"
    - "an information theoretic perspective of the poisson approximation via the chen stein method"
    - "on the entropy of sums of bernoulli random variables via the chen stein method"

Connected through hyperedge HE-3 (induced by source paper "interactive computation of type threshold functions in collocated broadcast superposition networks"):
  with members:
    - "computation over gaussian networks with orthogonal components"

Connected through hyperedge HE-4 (induced by source paper "improved lower bounds on the total variation distance for the poisson approximation"):
  with members:
    - "lower bounds on information divergence"

Connected through hyperedge HE-5 (induced by source paper "an information theoretic perspective of the poisson approximation via the chen stein method"):
  with members:
    - "lower bounds on information divergence"
    - "monotonicity thinning and discrete versions of the entropy power inequality"
    - "thinning entropy and the law of thin numbers"
    - "monotonic convergence in an information theoretic law of small numbers"

Connected through hyperedge HE-6 (induced by source paper "on the entropy of sums of bernoulli random variables via the chen stein method"):
  with members:
    - "an information theoretic perspective of the poisson approximation via the chen stein method"
    - "monotonicity thinning and discrete versions of the entropy power inequality"
    - "thinning entropy and the law of thin numbers"
    - "monotonic convergence in an information theoretic law of small numbers"

Connected through hyperedge HE-7 (induced by source paper "thinning entropy and the law of thin numbers"):
  with members:
    - "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)