human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "capacity of a nonlinear optical channel with finite memory"
  Abstract: The channel capacity of a nonlinear, dispersive fiber-optic link is revisited. To this end, the popular Gaussian noise (GN) model is extended with a parameter to account for the finite memory of realistic fiber channels. This finite-memory model is harder to analyze mathematically but, in contrast to previous models, it is valid also for nonstationary or heavy-tailed input signals. For uncoded transmission and standard modulation formats, the new model gives the same results as the regular GN model when the memory of the channel is about ten symbols or more. These results confirm previous results that the GN model is accurate for uncoded transmission. However, when coding is considered, the results obtained using the finite-memory model are very different from those obtained by previous models, even when the channel memory is large. In particular, the peaky behavior of the channel capacity, which has been reported for numerous nonlinear channel models, appears to be an artifact of applying models derived for independent input in a coded (i.e., dependent) scenario.

Connected through hyperedge HE-1 (induced by source paper "the log log growth of channel capacity for nondispersive nonlinear optical fiber channel in intermediate power range extension of the model"):
  with members:
    - "autocorrelation function for dispersion free fiber channels with distributed amplification"
    - "next to leading order corrections to capacity for nondispersive nonlinear optical fiber channel in intermediate power region"
    - "calculation of mutual information for nonlinear communication channel at large signal to noise ratio"
    - "on the per sample capacity of nondispersive optical fibers"

Connected through hyperedge HE-2 (induced by source paper "improved lower bounds on mutual information accounting for nonlinear signal noise interaction"):
  with members:
    - "information rates of next generation long haul optical fiber systems using coded modulation"
    - "impact of 4d channel distribution on the achievable rates in coherent optical communication experiments"
    - "stochastic digital backpropagation with residual memory compensation"
    - "upper bound on the capacity of a cascade of nonlinear and noisy channels"
    - "on achievable rates for long haul fiber optic communications"
    - "optimization of information rate upper and lower bounds for channels with memory"

Connected through hyperedge HE-3 (induced by source paper "non parametric estimation of mutual information with application to nonlinear optical fibers"):
  with members:
    - "improved lower bounds on mutual information accounting for nonlinear signal noise interaction"
    - "a non parametric k nearest neighbour entropy estimator"
    - "upper bound on the capacity of a cascade of nonlinear and noisy channels"
    - "mutual information as a figure of merit for optical fiber systems"

Connected through hyperedge HE-4 (induced by source paper "performance prediction of nonbinary forward error correction in optical transmission experiments"):
  with members:
    - "information rates of next generation long haul optical fiber systems using coded modulation"
    - "sensitivity gains by mismatched probabilistic shaping for optical communication systems"
    - "advances in detection and error correction for coherent optical communications regular irregular and spatially coupled ldpc code designs"
    - "impact of 4d channel distribution on the achievable rates in coherent optical communication experiments"
    - "on achievable rates for long haul fiber optic communications"
    - "four dimensional coded modulation with bit wise decoders for future optical communications"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

Connected through hyperedge HE-5 (induced by source paper "information rates of next generation long haul optical fiber systems using coded modulation"):
  with members:
    - "sensitivity gains by mismatched probabilistic shaping for optical communication systems"
    - "impact of 4d channel distribution on the achievable rates in coherent optical communication experiments"
    - "upper bound on the capacity of a cascade of nonlinear and noisy channels"
    - "replacing the soft fec limit paradigm in the design of optical communication systems"
    - "on achievable rates for long haul fiber optic communications"
    - "four dimensional coded modulation with bit wise decoders for future optical communications"
    - "staircase codes fec for 100 gb s otn"

Connected through hyperedge HE-6 (induced by source paper "impact of 4d channel distribution on the achievable rates in coherent optical communication experiments"):
  with members:
    - "upper bound on the capacity of a cascade of nonlinear and noisy channels"
    - "on achievable rates for long haul fiber optic communications"
    - "four dimensional coded modulation with bit wise decoders for future optical communications"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

Connected through hyperedge HE-7 (induced by source paper "models and information rates for multiuser optical fiber channels with nonlinearity and dispersion"):
  with members:
    - "real interference alignment exploiting the potential of single antenna systems"
    - "information transmission using the nonlinear fourier transform part ii numerical methods"
    - "information transmission using the nonlinear fourier transform part i mathematical tools"
    - "information transmission using the nonlinear fourier transform part iii spectrum modulation"
    - "interference focusing for simplified optical fiber models with dispersion"
    - "on the per sample capacity of nondispersive optical fibers"
    - "interference focusing for mitigating cross phase modulation in a simplified optical fiber model"

Connected through hyperedge HE-8 (induced by source paper "a lower bound on the per soliton capacity of the nonlinear optical fibre channel"):
  with members:
    - "upper bound on the capacity of a cascade of nonlinear and noisy channels"
    - "upper bound on the capacity of the nonlinear schr odinger channel"
    - "information transmission using the nonlinear fourier transform part ii numerical methods"
    - "information transmission using the nonlinear fourier transform part i mathematical tools"
    - "information transmission using the nonlinear fourier transform part iii spectrum modulation"
    - "on the achievable communication rates of generalized soliton transmission systems"

Question: Please tell me which class the center node 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)