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 "experimental comparison of probabilistic shaping methods for unrepeated fiber transmission"):
  Abstract: This paper studies the impact of probabilistic shaping on effective signal-to-noise ratios (SNRs) and achievable information rates (AIRs) in a back-to-back configuration and in unrepeated nonlinear fiber transmissions. For the back-to-back setup, various shaped quadrature amplitude modulation (QAM) distributions are found to have the same implementation penalty as uniform input. By demonstrating in transmission experiments that shaped QAM input leads to lower effective SNR than uniform input at a fixed average launch power, we experimentally confirm that shaping enhances the fiber nonlinearities. However, shaping is ultimately found to increase the AIR, which is the most relevant figure of merit, as it is directly related to spectral efficiency. In a detailed study of these shaping gains for the nonlinear fiber channel, four strategies for optimizing QAM input distributions are evaluated and experimentally compared in wavelength division multiplexing (WDM) systems. The first shaping scheme generates a Maxwell–Boltzmann (MB) distribution based on a linear additive white Gaussian noise channel. The second strategy uses the Blahut–Arimoto algorithm to optimize an unconstrained QAM distribution for a split-step Fourier method based channel model. In the third and fourth approach, MB-shaped QAM and unconstrained QAM are optimized via the enhanced Gaussian noise (EGN) model. Although the absolute shaping gains are found to be relatively small, the relative improvements by EGN-optimized unconstrained distributions over linear AWGN optimized MB distributions are up to 59%. This general behavior is observed in 9-channel and fully loaded WDM experiments.

Includes members:
  Member "sensitivity gains by mismatched probabilistic shaping for optical communication systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "markov chain monte carlo methods for lattice gaussian sampling convergence analysis and enhancement")
  Member "impact of 4d channel distribution on the achievable rates in coherent optical communication experiments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nonlinearity mitigation in wdm systems models strategies and achievable rates")
  Member "on probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "analysis and optimization of tail biting spatially coupled protograph ldpc codes for bicm id systems")
  Member "constellation shaping for wdm systems using 256qam 1024qam with probabilistic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "introducing enumerative sphere shaping for optical communication systems with short blocklengths")
  Member "constant composition distribution matching":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rate constrained shaping codes for structured sources")
  Member "ldpc coded modulation with probabilistic shaping for optical fiber systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "exploring and experimenting with shaping designs for next generation optical communications")
  Member "four dimensional coded modulation with bit wise decoders for future optical communications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "introducing enumerative sphere shaping for optical communication systems with short blocklengths")
  Member "a pragmatic coded modulation scheme for high spectral efficiency fiber optic communications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sign bit shaping using polar codes")

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)