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 "generalized degrees of freedom of noncoherent diamond networks"):
  Abstract: We study the generalized degrees of freedom (gDoF) of the block-fading noncoherent diamond (parallel relay) wireless network with asymmetric distributions of link strengths, and a coherence time of T symbol duration. We first derive an outer bound for this channel and then derive the optimal signaling structure for this outer bound. Using the optimal signaling structure we solve the outer bound optimization problem in terms of its gDoF. Using insights from our outer bound signaling solution, we devise an achievability strategy based on a novel scheme that we call train-scale quantize-map-forward (TS-QMF). This uses training in the links from the source to the relays, scaling and quantizing at the relays combined with nontraining-based schemes. We show the optimality of this scheme with respect to the outer bound in terms of the gDoF. In noncoherent point-to-point multiple-input-multiple-output (MIMO) channels, where the fading channel is unknown to transmitter and receiver, an important tradeoff between communication and channel learning was revealed by Zheng and Tse, by demonstrating that not all the available antennas might be used, as it is suboptimal to learn all their channel parameters. Our results in this paper for the diamond network demonstrates that in certain regimes the optimal scheme uses a subnetwork, demonstrating a tradeoff between channel learning and communications. In some regimes, it is gDoF optimal to do relay selection, i.e, use a part of the network. In the other regimes, even when it is essential to use the entire network, it is suboptimal to learn the channel states for all the links in the network, i.e, traditional training-based schemes are suboptimal in these regimes.

Includes members:
  Member "generalized degrees freedom of noncoherent mimo with asymmetric links":
    also connected through other hyperedges:
      (none)
  Member "capacity approximations for gaussian relay networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the arbitrarily varying relay channel")
  Member "cooperative relaying at finite snr role of quantize map and forward":
    also connected through other hyperedges:
      (none)
  Member "wireless network information flow a deterministic approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "best relay selection in gaussian half duplex diamond networks")
  Member "the approximate capacity of the gaussian n relay diamond network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "energy efficiency gains of caching for interference channels")
  Member "on noncoherent fading relay channels at high signal to noise ratio":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the generalized degrees of freedom of noncoherent interference channel")
  Member "wireless network simplification the gaussian n relay diamond network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "best relay selection in gaussian half duplex diamond networks")
  Member "approximately achieving gaussian relay network capacity with lattice codes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the generalized degrees of freedom of noncoherent interference channel")

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)