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 "cooperative relaying at finite snr role of quantize map and forward"):
  Abstract: Quantize-Map-and-Forward (QMF) relaying has been shown to achieve the optimal diversity-multiplexing trade-off (DMT) for arbitrary slow fading full-duplex networks as well as for the single-relay half-duplex network. A key reason for the DMT-optimality of QMF is that quantizing at the noise level suffices to achieve the cut-set bound approximately to within an additive gap, without the requirement of any instantaneous channel state information (CSI). However, DMT only captures the high SNR performance and potentially, limited CSI at the relay can help improve the performance in moderate SNR regimes. In this work we propose an optimization framework for QMF relaying over slow fading channels. Focusing on vector Gaussian quantizers, we optimize the outage probability for the full-duplex single relay by finding the best quantization level according to the available CSI at the relays. For the half-duplex relay channel, we find jointly optimal quantizer distortions and relay schedules using the same framework. Also, for the $N$-relay diamond network, we derive an universal quantizer that uses only the information of network topology. The universal quantizer sharpens the additive approximation gap of QMF from the conventional $\Theta(N)$ bits/s/Hz to $\Theta(\log(N))$ bits/s/Hz. Analytical solutions to channel-aware optimal quantizers for two-relay and symmetric $N$-relay diamond networks are also derived. In addition, we prove that suitable hybridizations of our optimized QMF schemes with Decode-Forward (DF) or Dynamic DF protocols provides significant finite SNR gains over the individual schemes.

Includes members:
  Member "achieving the capacity of the n relay gaussian diamond network within log n bits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the arbitrarily varying relay channel")
  Member "experiment driven characterization of full duplex wireless systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "simultaneous downlink data transmission and uplink channel estimation with reduced complexity full duplex mimo radios")
  Member "wireless network information flow a deterministic approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "generalized degrees of freedom of noncoherent diamond networks")
  Member "compute and forward harnessing interference through structured codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "private information retrieval over gaussian mac")
  Member "diversity multiplexing tradeoff of the half duplex relay channel":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "when are dynamic relaying strategies necessary in half duplex wireless networks")

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)