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 "minimum kl divergence on complements of l_1 balls"):
  Abstract: Pinsker's widely used inequality upper-bounds the total variation distance $||P-Q||_1$ in terms of the Kullback-Leibler divergence $D(P||Q)$. Although in general a bound in the reverse direction is impossible, in many applications the quantity of interest is actually $D^*(P,\eps)$ --- defined, for an arbitrary fixed $P$, as the infimum of $D(P||Q)$ over all distributions $Q$ that are $\eps$-far away from $P$ in total variation. We show that $D^*(P,\eps)\le C\eps^2 + O(\eps^3)$, where $C=C(P)=1/2$ for "balanced" distributions, thereby providing a kind of reverse Pinsker inequality. An application to large deviations is given, and some of the structural results may be of independent interest. Keywords: Pinsker inequality, Sanov's theorem, large deviations

Includes members:
  Member "renyi divergence and kullback leibler divergence":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "on pinsker s and vajda s type inequalities for csiszar s f divergences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "theoretical evidence for adversarial robustness through randomization")
  Member "on pairs of f divergences and their joint range":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a better bound gives a hundred rounds enhanced privacy guarantees via f divergences")
  Member "generalised pinsker inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "guaranteed deterministic bounds on the total variation distance between univariate mixtures")

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)