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: "on the per sample capacity of nondispersive optical fibers"
  Abstract: The capacity of the channel defined by the stochastic nonlinear Schrodinger equation, which includes the effects of the Kerr nonlinearity and amplified spontaneous emission noise, is considered in the case of zero dispersion. In the absence of dispersion, this channel behaves as a collection of parallel per-sample channels. The conditional probability density function of the nonlinear per-sample channels is derived using both a sum-product and a Fokker-Planck differential equation approach. It is shown that, for a fixed noise power, the per-sample capacity grows unboundedly with input signal. The channel can be partitioned into amplitude and phase subchannels, and it is shown that the contribution to the total capacity of the phase channel declines for large input powers. It is found that a 2-D distribution with a half-Gaussian profile on the amplitude and uniform phase provides a lower bound for the zero-dispersion optical fiber channel, which is simple and asymptotically capacity-achieving at high signal-to-noise ratios (SNRs). A lower bound on the capacity is also derived in the medium-SNR region. The exact capacity subject to peak and average power constraints is numerically quantified using dense multiple ring modulation formats. The differential model underlying the zero-dispersion channel is reduced to an algebraic model, which is more tractable for digital communication studies, and, in particular, it provides a relation between the zero-dispersion optical channel and a 2 × 2 multiple-input multiple-output Rician fading channel. It appears that the structure of the capacity-achieving input distribution resembles that of the Rician fading channel, i.e., it is discrete in amplitude with a finite number of mass points, while continuous and uniform in phase.

Connected through hyperedge HE-1 (induced by source paper "a signal space distance measure for nondispersive optical fiber"):
  with members:
    - "towards a non stochastic information theory"
    - "autocorrelation function for dispersion free fiber channels with distributed amplification"
    - "fast numerical nonlinear fourier transforms"
    - "upper bound on the capacity of the nonlinear schr odinger channel"
    - "information transmission using the nonlinear fourier transform part iii spectrum modulation"
    - "design of apsk constellations for coherent optical channels with nonlinear phase noise"
    - "a nonstochastic information theory for communication and state estimation"
    - "optimizing constellations for single subcarrier intensity modulated optical systems"

Connected through hyperedge HE-2 (induced by source paper "nonlinearity mitigation in wdm systems models strategies and achievable rates"):
  with members:
    - "improved lower bounds on mutual information accounting for nonlinear signal noise interaction"
    - "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"
    - "on probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel"
    - "upper bound on the capacity of a cascade of nonlinear and noisy channels"
    - "on achievable rates for long haul fiber optic communications"
    - "influence of behavioral models on multiuser channel capacity"

Connected through hyperedge HE-3 (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"
    - "capacity of a nonlinear optical channel with finite memory"

Connected through hyperedge HE-4 (induced by source paper "achievable information rates for nonlinear fiber communication via end to end autoencoder learning"):
  with members:
    - "end to end deep learning of optical fiber communications"
    - "an introduction to deep learning for the physical layer"
    - "nonlinear interference mitigation via deep neural networks"
    - "on achievable rates for long haul fiber optic communications"
    - "design of apsk constellations for coherent optical channels with nonlinear phase noise"

Connected through hyperedge HE-5 (induced by source paper "autocorrelation function for dispersion free fiber channels with distributed amplification"):
  with members:
    - "capacity achieving input distributions in nondispersive optical fibers"
    - "upper bound on the capacity of a cascade of nonlinear and noisy channels"
    - "capacity bounds for discrete time amplitude constrained additive white gaussian noise channels"
    - "optimal input signal distribution and per sample mutual information for nondispersive nonlinear optical fiber channel in large snr limit"
    - "on continuous time white phase noise channels"
    - "calculation of mutual information for partially coherent gaussian channels with applications to fiber optics"

Connected through hyperedge HE-6 (induced by source paper "demodulation and detection schemes for a memoryless optical wdm channel"):
  with members:
    - "information transmission using the nonlinear fourier transform part iii spectrum modulation"
    - "design of apsk constellations for coherent optical channels with nonlinear phase noise"
    - "interference focusing for simplified optical fiber models with dispersion"

Connected through hyperedge HE-7 (induced by source paper "linear and nonlinear frequency division multiplexing"):
  with members:
    - "a novel detection strategy for nonlinear frequency division multiplexing"
    - "decision feedback detection strategy for nonlinear frequency division multiplexing"
    - "communication using eigenvalues of higher multiplicity of the nonlinear fourier transform"
    - "the kolmogorov zakharov model for optical fiber communication"
    - "correlated eigenvalues optical communications"
    - "why noise and dispersion may seriously hamper nonlinear frequency division multiplexing"
    - "polarization division multiplexing based on the nonlinear fourier transform"
    - "nonlinear frequency division multiplexing in the focusing regime"

Connected through hyperedge HE-8 (induced by source paper "achievable rates of multidimensional rotationally invariant distributions"):
  with members:
    - "four dimensional coded modulation with bit wise decoders for future optical communications"
    - "interference focusing for simplified optical fiber models with dispersion"
    - "the noncoherent rician fading channel part i structure of the capacity achieving input"

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)