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: "impact of 4d channel distribution on the achievable rates in coherent optical communication experiments"
  Abstract: We experimentally investigate mutual information and generalized mutual information for coherent optical transmission systems. The impact of the assumed channel distribution on the achievable rate is investigated for distributions in up to four dimensions. Single channel and wavelength-division multiplexing (WDM) transmission over transmission links with and without inline dispersion compensation are studied. We show that for conventional WDM systems without inline dispersion compensation, a circularly symmetric complex Gaussian distribution is a good approximation of the channel. For other channels, such as with inline dispersion compensation, this is no longer true and gains in the achievable information rate are obtained by considering more sophisticated four-dimensional (4D) distributions. We also show that for nonlinear channels, gains in the achievable information rate can also be achieved by estimating the mean values of the received constellation in four dimensions. The highest gain for such channels is seen for a 4D correlated Gaussian distribution.

Connected through hyperedge HE-1 (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"
    - "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"
    - "on the per sample capacity of nondispersive optical fibers"

Connected through hyperedge HE-2 (induced by source paper "how to increase the achievable information rate by per channel dispersion compensation"):
  with members:
    - "improved lower bounds on mutual information accounting for nonlinear signal noise interaction"
    - "demodulation and detection schemes for a memoryless optical wdm channel"

Connected through hyperedge HE-3 (induced by source paper "geometric constellation shaping for fiber optic communication systems via end to end learning"):
  with members:
    - "experimental demonstration of geometrically shaped constellations tailored to the nonlinear fibre channel"
    - "a survey on application of machine learning techniques in optical networks"
    - "deep learning of the nonlinear schr odinger equation in fiber optic communications"
    - "end to end learning of communications systems without a channel model"
    - "deep learning of geometric constellation shaping including fiber nonlinearities"
    - "an introduction to deep learning for the physical layer"
    - "applying neural networks in optical communication systems possible pitfalls"
    - "constellation shaping for wdm systems using 256qam 1024qam with probabilistic optimization"

Connected through hyperedge HE-4 (induced by source paper "exploring and experimenting with shaping designs for next generation optical communications"):
  with members:
    - "experimental comparison of probabilistic shaping methods for unrepeated fiber transmission"
    - "golden angle modulation"
    - "on probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel"
    - "constellation shaping for wdm systems using 256qam 1024qam with probabilistic optimization"
    - "experimental demonstration of capacity increase and rate adaptation by probabilistically shaped 64 qam"
    - "ldpc coded modulation with probabilistic shaping for optical fiber systems"
    - "achieving awgn channel capacity with lattice gaussian coding"

Connected through hyperedge HE-5 (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"
    - "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"
    - "capacity of a nonlinear optical channel with finite memory"
    - "optimization of information rate upper and lower bounds for channels with memory"

Connected through hyperedge HE-6 (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"
    - "on achievable rates for long haul fiber optic communications"
    - "four dimensional coded modulation with bit wise decoders for future optical communications"
    - "capacity of a nonlinear optical channel with finite memory"
    - "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-7 (induced by source paper "experimental comparison of probabilistic shaping methods for unrepeated fiber transmission"):
  with members:
    - "sensitivity gains by mismatched probabilistic shaping for optical communication systems"
    - "on probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel"
    - "constellation shaping for wdm systems using 256qam 1024qam with probabilistic optimization"
    - "constant composition distribution matching"
    - "ldpc coded modulation with probabilistic shaping for optical fiber systems"
    - "four dimensional coded modulation with bit wise decoders for future optical communications"
    - "a pragmatic coded modulation scheme for high spectral efficiency fiber optic communications"

Connected through hyperedge HE-8 (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"
    - "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"
    - "capacity of a nonlinear optical channel with finite memory"
    - "staircase codes fec for 100 gb s otn"

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)