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 "multinomial concentration in relative entropy at the ratio of alphabet and sample sizes"):
  Abstract: We show that the moment generating function of the Kullback-Leibler divergence between the empirical distribution of $n$ independent samples from a distribution $P$ over a finite alphabet of size $k$ (e.g. a multinomial distribution) and $P$ itself is no more than that of a gamma distribution with shape $k - 1$ and rate $n$. The resulting exponential concentration inequality becomes meaningful (less than 1) when the divergence $\varepsilon$ is larger than $(k-1)/n$, whereas the standard method of types bound requires $\varepsilon > \frac{1}{n} \cdot \log{\binom{n+k-1}{k-1}} \geq (k-1)/n \cdot \log(1 + n/(k-1))$, thus saving a factor of order $\log(n/k)$ in the standard regime of parameters where $n\gg k$. Our proof proceeds via a simple reduction to the case $k = 2$ of a binary alphabet (e.g. a binomial distribution), and has the property that improvements in the case of $k = 2$ directly translate to improvements for general $k$.

Includes members:
  Member "samplers and extractors for unbounded functions":
    also connected through other hyperedges:
      (none)
  Member "on the robustness of information theoretic privacy measures and mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "obfuscation via information density estimation")
  Member "concentration inequalities for the empirical distribution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal estimation of change in a population of parameters")
  Member "maximum likelihood estimation of functionals of discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "importance of small probability events in big data information measures applications and challenges")

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)