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 "on the complexity of estimating renyi divergences"):
  Abstract: This paper studies the complexity of estimating Renyi divergences of a distribution $p$ observed from samples, with respect to a baseline distribution $q$ known \emph{a priori}. Extending the results of Acharya et al. (SODA'15) on estimating Renyi entropy, we present improved estimation techniques together with upper and lower bounds on the sample complexity. #R##N#We show that, contrarily to estimating Renyi entropy where a sublinear (in the alphabet size) number of samples suffices, the sample complexity is heavily dependent on \emph{events occurring unlikely} in $q$, and is unbounded in general (no matter what an estimation technique is used). For any divergence of order bigger than $1$, we provide upper and lower bounds on the number of samples dependent on probabilities of $p$ and $q$. We conclude that the worst-case sample complexity is polynomial in the alphabet size if and only if the probabilities of $q$ are non-negligible. #R##N#This gives theoretical insights into heuristics used in applied papers to handle numerical instability, which occurs for small probabilities of $q$. Our result explains that small probabilities should be handled with care not only because of numerical issues, but also because of a blow up in sample complexity.

Includes members:
  Member "generalized exponential concentration inequality for r enyi divergence estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the estimation of information measures of continuous distributions")
  Member "testing shape restrictions of discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "monotone probability distributions over the boolean cube can be learned with sublinear samples")
  Member "renyi divergence and kullback leibler divergence":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")

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)