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 "brascamp lieb inequality and its reverse an information theoretic view"):
  Abstract: We generalize a result by Carlen and CorderoErausquin on the equivalence between the Brascamp-Lieb inequality and the subadditivity of relative entropy by allowing for random transformations (a broadcast channel). This leads to a unified perspective on several functional inequalities that have been gaining popularity in the context of proving impossibility results. We demonstrate that the information theoretic dual of the Brascamp-Lieb inequality is a convenient setting for proving properties such as data processing, tensorization, convexity and Gaussian optimality. Consequences of the latter include an extension of the Brascamp-Lieb inequality allowing for Gaussian random transformations, the determination of the multivariate Wyner common information for Gaussian sources, and a multivariate version of Nelson’s hypercontractivity theorem. Finally we present an information theoretic characterization of a reverse Brascamp-Lieb inequality involving a random transformation (a multiple access channel).

Includes members:
  Member "information theoretic perspectives on brascamp lieb inequality and its reverse":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "second order converses via reverse hypercontractivity")
  Member "converses for distributed estimation via strong data processing inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "information theoretic perspectives on brascamp lieb inequality and its reverse")
  Member "secret key generation with one communicator and a one shot converse via hypercontractivity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "communication for generating correlation a unifying survey")
  Member "a note on the strong data processing inequalities in bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "comparison of channels criteria for domination by a symmetric channel")
  Member "communication lower bounds for statistical estimation problems via a distributed data processing inequality":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "locally private hypothesis selection")
  Member "an extremal inequality for long markov chains":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "extracting robust and accurate features via a robust information bottleneck")
  Member "information theoretic applications of the logarithmic probability comparison bound":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "information theoretic perspectives on brascamp lieb inequality and its reverse")
  Member "key capacity for product sources with application to stationary gaussian processes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "communication for generating correlation a unifying survey")

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)