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 generalized degrees of freedom of noncoherent interference channel"):
  Abstract: We study the generalized degrees of freedom (gDoF) of the block-fading noncoherent 2-user interference channel (IC) with a coherence time of T symbol durations and symmetric fading statistics. We demonstrate that a natural training-based scheme, to operate the noncoherent IC, is suboptimal in several regimes. As an alternate scheme, we propose a new noncoherent rate-splitting scheme. We also consider treating interference-as-noise (TIN) scheme and a time division multiplexing (TDM) scheme. We observe that a standard training-based scheme for IC is outperformed by one of these schemes in several regimes: our results demonstrate that for low average interference-to-noise ratio (INR), TIN is best; for high INR, TDM and the noncoherent rate-splitting give better performance. We also study the noncoherent IC with feedback and propose a noncoherent rate-splitting scheme. Again for the feedback case as well, our results demonstrate that a natural training-based scheme can be outperformed by other schemes.

Includes members:
  Member "approximate capacity of fast fading interference channels with no instantaneous csit":
    also connected through other hyperedges:
      (none)
  Member "on noncoherent fading relay channels at high signal to noise ratio":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generalized degrees of freedom of noncoherent diamond networks")
  Member "interference channels with rate limited feedback":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "capacity results for intermittent x channels with delayed channel state feedback")
  Member "feedback capacity of the gaussian interference channel to within 2 bits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "capacity limits of full duplex cellular network")
  Member "approximately achieving gaussian relay network capacity with lattice codes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generalized degrees of freedom of noncoherent diamond networks")
  Member "gaussian interference channel capacity to within one bit":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fundamental limits of wireless caching under mixed cacheable and uncacheable traffic")

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)