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: "performance prediction of nonbinary forward error correction in optical transmission experiments"
  Abstract: In this paper, we compare different metrics to predict the error rate of optical systems based on nonbinary forward error correction (FEC). It is shown that an accurate metric to predict the performance of coded modulation based on nonbinary FEC is the mutual information. The accuracy of the prediction is verified in a detailed example with multiple constellation formats and FEC overheads, in both simulations and optical transmission experiments over a recirculating loop. It is shown that the employed FEC codes must be universal if performance prediction based on thresholds is used. A tutorial introduction into the computation of the thresholds from optical transmission measurements is also given.

Connected through hyperedge HE-1 (induced by source paper "achievable information rates for coded modulation with hard decision decoding for coherent fiber optic systems"):
  with members:
    - "information rates of next generation long haul optical fiber systems using coded modulation"
    - "density evolution for deterministic generalized product codes with higher order modulation"
    - "on achievable rates for long haul fiber optic communications"
    - "terminated and tailbiting spatially coupled codes with optimized bit mappings for spectrally efficient fiber optical systems"
    - "staircase codes fec for 100 gb s otn"
    - "approaching capacity at high rates with iterative hard decision decoding"

Connected through hyperedge HE-2 (induced by source paper "performance metrics for systems with soft decision fec and probabilistic shaping"):
  with members:
    - "achievable rates for probabilistic shaping"
    - "sensitivity gains by mismatched probabilistic shaping for optical communication systems"
    - "constant composition distribution matching"
    - "on the information loss of the max log approximation in bicm systems"

Connected through hyperedge HE-3 (induced by source paper "capacity lower bounds of the noncentral chi channel with applications to soliton amplitude modulation"):
  with members:
    - "nonlinear frequency division multiplexing"
    - "nonlinear frequency division multiplexing in the focusing regime"
    - "a lower bound on the per soliton capacity of the nonlinear optical fibre channel"
    - "upper bound on the capacity of a cascade of nonlinear and noisy channels"
    - "fast numerical nonlinear fourier transforms"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "an experimental comparison of coded modulation strategies for 100 gb s transceivers"):
  with members:
    - "impact of 4d channel distribution on the achievable rates in coherent optical communication experiments"
    - "on the impact of optimal modulation and fec overhead on future optical networks"

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)