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 "bounds among f divergences"):
  Abstract: This paper develops methods to obtain bounds on an $f$-divergence based on one or several other $f$-divergences, dealing with pairs of probability measures defined on arbitrary alphabets. Functional domination is one such approach, where special emphasis is placed on finding the best possible constant upper bounding a ratio of $f$-divergences. Another approach used for the derivation of bounds among $f$-divergences relies on moment inequalities and the logarithmic-convexity property, which is studied in this paper to obtain tight bounds on the relative entropy and Bhattacharyya distance in terms of $\chi^2$ divergences. A rich variety of bounds are shown to hold under boundedness assumptions on the relative information. Special attention is on the total variation distance and its relation to the relative information and relative entropy, including "reverse Pinsker inequalities," as well as on the $E_\gamma$ divergence, which generalizes the total variation distance. Pinsker's inequality is extended for this type of $f$-divergence, a result which leads to an inequality that links the relative entropy and relative information spectrum. Integral expressions of the R\'enyi divergence in terms of the relative information spectrum are derived, leading to bounds on the R\'enyi divergence in terms of either the variational distance or relative entropy.

Includes members:
  Member "strong converse theorems for classes of multimessage multicast networks a renyi divergence approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the renyi capacity and center")
  Member "f divergence inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on implicit regularization in beta vaes")
  Member "optimal quantization for distribution synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a note on reverse pinsker inequalities")
  Member "on the r e nyi divergence and the joint range of relative entropies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "upper bounds on the relative entropy and r enyi divergence as a function of total variation distance for finite alphabets")
  Member "one shot mutual covering lemma and marton s inner bound with a common message":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sharp bounds for mutual covering")
  Member "on the renyi divergence joint range of relative entropies and a channel coding theorem":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the renyi capacity and center")
  Member "resolvability in e γ with applications to lossy compression and wiretap channels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "strong functional representation lemma and applications to coding theorems")
  Member "tight bounds for symmetric divergence measures and a refined bound for lossless source coding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "limits of deepfake detection a robust estimation viewpoint")

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)