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 probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel"):
  Abstract: Different aspects of probabilistic shaping for a multispan optical communication system are studied. First, a numerical analysis of the additive white Gaussian noise (AWGN) channel investigates the effect of using a small number of input probability mass functions (PMFs) for a range of signal-to-noise ratios (SNRs), instead of optimizing the constellation shaping for each SNR. It is shown that if a small penalty of at most 0.1 dB SNR to the full shaping gain is acceptable, just two shaped PMFs are required per quadrature amplitude modulation (QAM) over a large SNR range. For a multispan wavelength division multiplexing optical fiber system with 64QAM input, it is shown that just one PMF is required to achieve large gains over uniform input for distances from 1400 to 3000 km. Using recently developed theoretical models that extend the Gaussian noise (GN) model and full-field split-step simulations, we illustrate the ramifications of probabilistic shaping on the effective SNR after fiber propagation. Our results show that, for a fixed average optical launch power, a shaping gain is obtained for the noise contributions from fiber amplifiers and modulation-independent nonlinear interference (NLI), whereas shaping simultaneously causes a penalty as it leads to an increased NLI. However, this nonlinear shaping loss is found to have a relatively minor impact, and optimizing the shaped PMF with a modulation-dependent GN model confirms that the PMF found for AWGN is also a good choice for a multi-span fiber system.

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 "constellation shaping for wdm systems using 256qam 1024qam with probabilistic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "introducing enumerative sphere shaping for optical communication systems with short blocklengths")
  Member "experimental demonstration of capacity increase and rate adaptation by probabilistically shaped 64 qam":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "finite precision implementation of arithmetic coding based distribution matchers")
  Member "on achievable rates for long haul fiber optic communications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "nonlinearity mitigation in wdm systems models strategies and achievable rates")
  Member "constant composition distribution matching":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (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-8 (induced by source paper "introducing enumerative sphere shaping for optical communication systems with short blocklengths")

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)