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 probabilistic shaping of quadrature amplitude modulation for the nonlinear fiber channel"
  Abstract: Different aspects of probabilistic shaping for a multispan optical communication system are studied. First, a numerical analysis of the additive white Gaussian noise (AWGN) channel investigates the effect of using a small number of input probability mass functions (PMFs) for a range of signal-to-noise ratios (SNRs), instead of optimizing the constellation shaping for each SNR. It is shown that if a small penalty of at most 0.1 dB SNR to the full shaping gain is acceptable, just two shaped PMFs are required per quadrature amplitude modulation (QAM) over a large SNR range. For a multispan wavelength division multiplexing optical fiber system with 64QAM input, it is shown that just one PMF is required to achieve large gains over uniform input for distances from 1400 to 3000 km. Using recently developed theoretical models that extend the Gaussian noise (GN) model and full-field split-step simulations, we illustrate the ramifications of probabilistic shaping on the effective SNR after fiber propagation. Our results show that, for a fixed average optical launch power, a shaping gain is obtained for the noise contributions from fiber amplifiers and modulation-independent nonlinear interference (NLI), whereas shaping simultaneously causes a penalty as it leads to an increased NLI. However, this nonlinear shaping loss is found to have a relatively minor impact, and optimizing the shaped PMF with a modulation-dependent GN model confirms that the PMF found for AWGN is also a good choice for a multi-span fiber system.

Connected through hyperedge HE-1 (induced by source paper "analysis and optimization of tail biting spatially coupled protograph ldpc codes for bicm id systems"):
  with members:
    - "finite length construction of high performance spatially coupled codes via optimized partitioning and lifting"
    - "efficient search of compact qc ldpc and sc ldpc convolutional codes with large girth"
    - "improving the decoding threshold of tailbiting spatially coupled ldpc codes by energy shaping"
    - "compact qc ldpc block and sc ldpc convolutional codes for low latency communications"
    - "design and analysis of time invariant sc ldpc convolutional codes with small constraint length"
    - "wave like decoding of tail biting spatially coupled ldpc codes through iterative demapping"
    - "finite length analysis of spatially coupled regular ldpc ensembles on burst erasure channels"
    - "efficient multi dimensional mapping using qam constellations for bicm id"

Connected through hyperedge HE-2 (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"
    - "constellation shaping for wdm systems using 256qam 1024qam with probabilistic optimization"

Connected through hyperedge HE-3 (induced by source paper "joint learning of geometric and probabilistic constellation shaping"):
  with members:
    - "deep learning for channel coding via neural mutual information estimation"
    - "geometric constellation shaping for fiber optic communication systems via end to end learning"
    - "model free training of end to end communication systems"
    - "an introduction to deep learning for the physical layer"
    - "constant composition distribution matching"
    - "adam a method for stochastic optimization"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"
    - "on the partition function and random maximum a posteriori perturbations"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "hierarchical distribution matching a versatile tool for probabilistic shaping"):
  with members:
    - "constant composition distribution matching"

Connected through hyperedge HE-6 (induced by source paper "markov chain monte carlo methods for lattice gaussian sampling convergence analysis and enhancement"):
  with members:
    - "boosted kz and lll algorithms"
    - "lattice gaussian sampling by markov chain monte carlo bounded distance decoding and trapdoor sampling"
    - "sensitivity gains by mismatched probabilistic shaping for optical communication systems"
    - "polar codes and polar lattices for independent fading channels"
    - "achieving secrecy capacity of the gaussian wiretap channel with polar lattices"
    - "fifty years of mimo detection the road to large scale mimos"
    - "on the geometric ergodicity of metropolis hastings algorithms for lattice gaussian sampling"
    - "optimized markov chain monte carlo for signal detection in mimo systems an analysis of the stationary distribution and mixing time"

Connected through hyperedge HE-7 (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"
    - "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-8 (induced by source paper "information rates and error exponents for probabilistic amplitude shaping"):
  with members:
    - "achievable rates for probabilistic shaping"
    - "comparison of geometric and probabilistic shaping with application to atsc 3 0"
    - "optimal quantization for distribution synthesis"
    - "constant composition distribution matching"
    - "informational divergence and entropy rate on rooted trees with probabilities"

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)