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: "constellation shaping for wdm systems using 256qam 1024qam with probabilistic optimization"
  Abstract: In this paper, probabilistic shaping is numerically and experimentally investigated for increasing the transmission reach of wavelength division multiplexed (WDM) optical communication systems employing quadrature amplitude modulation (QAM). An optimized probability mass function (PMF) of the QAM symbols is first found from a modified Blahut–Arimoto algorithm for the optical channel. A turbo coded bit interleaved coded modulation system is then applied, which relies on many-to-one labeling to achieve the desired PMF, thereby achieving shaping gains. Pilot symbols at rate at most 2% are used for synchronization and equalization, making it possible to receive input constellations as large as 1024QAM. The system is evaluated experimentally on a 10 GBd, 5 channels WDM setup. The maximum system reach is increased w.r.t. standard 1024QAM by 20% at input data rate of 4.65 bits/symbol and up to 75% at 5.46 bits/symbol. It is shown that rate adaptation does not require changing of the modulation format. The performance of the proposed 1024QAM shaped system is validated on all 5 channels of the WDM signal for selected distances and rates. Finally, it is shown via EXIT charts and BER analysis that iterative demapping, while generally beneficial to the system, is not a requirement for achieving the shaping gain.

Connected through hyperedge HE-1 (induced by source paper "introducing enumerative sphere shaping for optical communication systems with short blocklengths"):
  with members:
    - "exploring and experimenting with shaping designs for next generation optical communications"
    - "divergence optimal fixed to fixed length distribution matching with shell mapping"
    - "experimental verification of rate flexibility and probabilistic shaping by 4d signaling"
    - "increasing achievable information rates via geometric shaping"
    - "experimental comparison of probabilistic shaping methods for unrepeated fiber transmission"
    - "a survey on fiber nonlinearity compensation for 400 gb s and beyond optical communication systems"
    - "comparison of geometric and probabilistic shaping with application to atsc 3 0"
    - "on probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel"

Connected through hyperedge HE-2 (induced by source paper "end to end learning for gmi optimized geometric constellation shape"):
  with members:
    - "increasing achievable information rates via geometric shaping"
    - "constant composition distribution matching"

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"
    - "impact of 4d channel distribution on the achievable rates in coherent optical communication experiments"

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"
    - "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"
    - "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 "deep learning of geometric constellation shaping including fiber nonlinearities"):
  with members:
    - "end to end deep learning of optical fiber communications"
    - "an introduction to deep learning for the physical layer"
    - "on probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel"

Connected through hyperedge HE-6 (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"
    - "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"
    - "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-7 (induced by source paper "on probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel"):
  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"
    - "experimental demonstration of capacity increase and rate adaptation by probabilistically shaped 64 qam"
    - "on achievable rates for long haul fiber optic communications"
    - "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"
    - "achievable rates for shaped bit metric decoding"

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)