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: "monotonic convergence in an information theoretic law of small numbers"
  Abstract: An "entropy increasing to the maximum" result analogous to the entropic central limit theorem (Barron 1986; Artstein 2004) is obtained in the discrete setting. This involves the thinning operation and a Poisson limit. Monotonic convergence in relative entropy is established for general discrete distributions, while monotonic increase of Shannon entropy is proved for the special class of ultra-log-concave distributions. Overall we extend the parallel between the information-theoretic central limit theorem and law of small numbers explored by Kontoyiannis (2005) and HarremoEumls (2007, 2008, 2009). Ingredients in the proofs include convexity, majorization, and stochastic orders.

Connected through hyperedge HE-1 (induced by source paper "forward and reverse entropy power inequalities in convex geometry"):
  with members:
    - "wasserstein stability of the entropy power inequality for log concave densities"
    - "on renyi entropy power inequalities"
    - "strengthening the entropy power inequality"
    - "variants of the entropy power inequality"
    - "entropy bounds on abelian groups and the ruzsa divergence"
    - "entropies of weighted sums in cyclic groups and an application to polar codes"
    - "beyond the entropy power inequality via rearrangements"
    - "a strengthened entropy power inequality for log concave densities"

Connected through hyperedge HE-2 (induced by source paper "thinning photonic beamsplitting and a general discrete entropy power inequality"):
  with members:
    - "a new entropy power inequality for integer valued random variables"
    - "monotonicity thinning and discrete versions of the entropy power inequality"
    - "concavity of entropy under thinning"
    - "generalized entropy power inequalities and monotonicity properties of information"

Connected through hyperedge HE-3 (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"
    - "sharp bounds on the entropy of the poisson law and related quantities"
    - "thinning entropy and the law of thin numbers"

Connected through hyperedge HE-4 (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"
    - "sharp bounds on the entropy of the poisson law and related quantities"
    - "thinning entropy and the law of thin numbers"

Connected through hyperedge HE-5 (induced by source paper "entropy power inequality for a family of discrete random variables"):
  with members:
    - "monotonicity thinning and discrete versions of the entropy power inequality"
    - "thinning entropy and the law of thin numbers"
    - "generalized entropy power inequalities and monotonicity properties of information"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-6 (induced by source paper "monotonicity thinning and discrete versions of the entropy power inequality"):
  with members:
    - "thinning entropy and the law of thin numbers"
    - "concavity of entropy under thinning"
    - "generalized entropy power inequalities and monotonicity properties of information"

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

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)