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 "unifying the brascamp lieb inequality and the entropy power inequality"):
  Abstract: The entropy power inequality (EPI) and the Brascamp-Lieb inequality (BLI) can be viewed as information inequalities concerning entropies of linear transformations of random variables. The EPI provides lower bounds for the entropy of linear transformations of random vectors with independent components. The BLI, on the other hand, provides upper bounds on the entropy of a random vector in terms of the entropies of its linear transformations. In this paper, we present a new entropy inequality that generalizes both the BLI and EPI by considering a variety of independence relations among the components of a random vector. Our main technical contribution is in the proof strategy that leverages the "doubling trick" to prove Gaussian optimality for certain entropy expressions under independence constraints.

Includes members:
  Member "strong data processing inequalities for channels and bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "yet another proof of the entropy power inequality":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transportation proofs of r enyi entropy power inequalities")
  Member "wasserstein continuity of entropy and outer bounds for interference channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a lower bound on the expected distortion of joint source channel coding")
  Member "superposition coding is almost always optimal for the poisson broadcast channel":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "extracting robust and accurate features via a robust information bottleneck")
  Member "an extremal inequality for long markov chains":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "extracting robust and accurate features via a robust information bottleneck")
  Member "information theoretic proofs of entropy power inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "vector gaussian successive refinement with degraded side information")

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)