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 "dependence balance based outer bounds for gaussian networks with cooperation and feedback"):
  Abstract: We obtain new outer bounds on the capacity regions of the two-user multiple access channel with generalized feedback (MAC-GF) and the two-user interference channel with generalized feedback (IC-GF). These outer bounds are based on the idea of dependence balance which was proposed by Hekstra and Willems. To illustrate the usefulness of our outer bounds, we investigate three different channel models. We first consider a Gaussian MAC with noisy feedback (MAC-NF), where transmitter k, k = 1, 2, receives a feedback Y(Fk), which is the channel output Y corrupted with additive white Gaussian noise Zk. For this channel model, the cut-set outer bound is not sensitive to the feedback noise variances. We demonstrate that our outer bound improves upon the cut-set bound for all nonzero values of the feedback noise variances. Moreover, in the limit as σ(Zk)2 → ∞, k = 1, 2, our outer bound collapses to the capacity region of the Gaussian MAC without feedback. Secondly, we investigate a Gaussian MAC with user-cooperation (MAC-UC), where each transmitter receives an additive white Gaussian noise corrupted version of the channel input of the other transmitter. For this channel model, the cut-set bound is sensitive to the cooperation noises, but not sensitive enough. For all nonzero values of cooperation noise variances, our outer bound strictly improves upon the cut-set outer bound. Moreover, as the cooperation noises become large, our outer bound collapses to the capacity region of the Gaussian MAC without cooperation. Thirdly, we investigate a Gaussian IC with user-cooperation (IC UC). For this channel model, the cut-set bound is again sensitive to cooperation noise variances as in the case of MAC-UC channel model, but not sensitive enough. We demonstrate that our outer bound strictly improves upon the cut-set bound for all nonzero values of cooperation noise variances.

Includes members:
  Member "interference channels with source cooperation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gdof of interference channel with limited cooperation under finite precision csit")
  Member "interference channels with destination cooperation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gdof of interference channel with limited cooperation under finite precision csit")
  Member "a multivariate generalization of costa s entropy power inequality":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a unifying variational perspective on some fundamental information theoretic inequalities")
  Member "the gaussian mac with conferencing encoders":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "duality of a source coding problem and the semi deterministic broadcast channel with rate limited cooperation")

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)