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: "concentration of the multinomial in kullback leibler divergence near the ratio of alphabet and sample sizes"
  Abstract: We bound the moment generating function of the Kullback-Leibler divergence between the empirical distribution of independent samples from a distribution over a finite alphabet (e.g. a multinomial distribution) and the underlying distribution via a simple reduction to the case of a binary alphabet (e.g. a binomial distribution). The resulting concentration inequality becomes meaningful (less than 1) when the deviation $\varepsilon$ is a constant factor larger than the ratio $(k-1)/n$ for $k$ the alphabet size and $n$ the number of samples, whereas the standard method of types bound requires $\varepsilon > (k-1)/n \cdot \log(1 + n/(k-1))$.

Connected through hyperedge HE-1 (induced by source paper "samplers and extractors for unbounded functions"):
  with members:
    - "fuzzy extractors how to generate strong keys from biometrics and other noisy data"

Connected through hyperedge HE-2 (induced by source paper "concentration inequalities for the empirical distribution"):
  with members:
    - "maximum likelihood estimation of functionals of discrete distributions"

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)