human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "performance metrics for systems with soft decision fec and probabilistic shaping"):
  Abstract: High-throughput optical communication systems utilize binary soft-decision forward error correction (SD-FEC) with bit interleaving over the bit channels. The generalized mutual information is an achievable information rate (AIR) in such systems and is known to be a good predictor of the bit error rate after SD-FEC decoding (post-FEC BER) for uniform signaling. However, for probabilistically shaped (nonuniform) signaling, we find that the normalized AIR, defined as the AIR divided by the signal entropy, is less correlated with the post-FEC BER. We show that the information quantity based on the distribution of the single bit signal, and its asymmetric log-likelihood ratio, are better predictors of the post-FEC BER. In simulations over the Gaussian channel, we find that the prediction accuracy, quantified as the peak-to-peak deviation of the post-FEC BER within a set of different modulation formats and distributions, can be improved more than ten times compared with the normalized AIR.

Includes members:
  Member "performance prediction of nonbinary forward error correction in optical transmission experiments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "achievable information rates for coded modulation with hard decision decoding for coherent fiber optic systems")
  Member "achievable rates for probabilistic shaping":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "achievable information rates for probabilistic amplitude shaping a minimum randomness approach via random sign coding arguments")
  Member "sensitivity gains by mismatched probabilistic shaping for optical communication systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "markov chain monte carlo methods for lattice gaussian sampling convergence analysis and enhancement")
  Member "constant composition distribution matching":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rate constrained shaping codes for structured sources")
  Member "on the information loss of the max log approximation in bicm systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "replacing the soft fec limit paradigm in the design of optical communication systems")

Question: Please tell me which class the source paper 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)