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 "nonlinearity mitigation in wdm systems models strategies and achievable rates"):
  Abstract: After reviewing models and mitigation strategies for interchannel nonlinear interference (NLI), we study its characteristics and coherence properties. Based on this study, we devise an NLI mitigation strategy, which exploits the synergic effect of phase and polarization noise (PPN) compensation and subcarrier multiplexing with symbol-rate optimization. This synergy persists even for high-order modulation alphabets and Gaussian symbols. A particle method for the computation of the resulting achievable information rate and spectral efficiency (SE) is presented and employed to lower-bound the channel capacity. The dependence of the SE on the link length, amplifier spacing, and presence or absence of in-line dispersion compensation is studied. Single-polarization and dual-polarization scenarios with either independent or joint processing of the two polarizations are considered. Numerical results show that, in links with ideal distributed amplification, an SE gain of about 1 bit/s/Hz/polarization can be obtained (or, in alternative, the system reach can be doubled at a given SE) with respect to single-carrier systems without PPN mitigation. The gain is lower with lumped amplification, increases with the number of spans, decreases with the span length, and is further reduced by in-line dispersion compensation. For instance, considering a dispersion-unmanaged link with lumped amplification and an amplifier spacing of 60 km, the SE after 80 spans can be be increased from 4.5 to 4.8 bit/s/Hz/polarization, or the reach raised up to 100 spans (+25%) for a fixed SE.

Includes members:
  Member "improved lower bounds on mutual information accounting for nonlinear signal noise interaction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "how to increase the achievable information rate by per channel dispersion compensation")
  Member "information rates of next generation long haul optical fiber systems using coded modulation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improved lower bounds on mutual information accounting for nonlinear signal noise interaction")
  Member "impact of 4d channel distribution on the achievable rates in coherent optical communication experiments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "how to increase the achievable information rate by per channel dispersion compensation")
  Member "on probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "analysis and optimization of tail biting spatially coupled protograph ldpc codes for bicm id systems")
  Member "upper bound on the capacity of a cascade of nonlinear and noisy channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improved lower bounds on mutual information accounting for nonlinear signal noise interaction")
  Member "on achievable rates for long haul fiber optic communications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improved lower bounds on mutual information accounting for nonlinear signal noise interaction")
  Member "influence of behavioral models on multiuser channel capacity":
    also connected through other hyperedges:
      (none)
  Member "on the per sample capacity of nondispersive optical fibers":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a signal space distance measure for nondispersive optical fiber")

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)