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 "achievable information rates for coded modulation with hard decision decoding for coherent fiber optic systems"):
  Abstract: We analyze the achievable information rates (AIRs) for coded modulation schemes with quadrature amplitude modulation constellations with both bitwise and symbolwise decoders, corresponding to the case where a binary code is used in combination with a higher order modulation using the bit-interleaved coded modulation (BICM) paradigm and to the case where a nonbinary code over a field matched to the constellation size is used, respectively. In particular, we consider hard decision decoding, which is the preferable option for fiber-optic communication systems where decoding complexity is a concern. Recently, Liga  et al.  analyzed the AIRs for bitwise and symbolwise decoders considering what the authors called  hard decision decoder , which, however, exploits   soft information  of the transition probabilities of discrete-input discrete-output channel resulting from the hard detection. As such, the complexity of the decoder is essentially the same as the complexity of a soft decision decoder. In this paper, we analyze instead the AIRs for the standard hard decision decoder, commonly used in practice, where the decoding is based on the Hamming distance metric. We show that if standard hard decision decoding is used, bitwise decoders yield significantly higher AIRs than the symbolwise decoders. As a result, contrary to the conclusion by Liga  et al. , binary decoders together with the BICM paradigm are preferable for spectrally efficient fiber-optic systems. We also design binary and nonbinary staircase codes and show that, in agreement with the AIRs, binary codes yield better performance.

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 "performance metrics for systems with soft decision fec and probabilistic shaping")
  Member "information rates of next generation long haul optical fiber systems using coded modulation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nonlinearity mitigation in wdm systems models strategies and achievable rates")
  Member "density evolution for deterministic generalized product codes with higher order modulation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on low complexity decoding of product codes for high throughput fiber optic systems")
  Member "on achievable rates for long haul fiber optic communications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nonlinearity mitigation in wdm systems models strategies and achievable rates")
  Member "terminated and tailbiting spatially coupled codes with optimized bit mappings for spectrally efficient fiber optical systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "analysis and optimization of tail biting spatially coupled protograph ldpc codes for bicm id systems")
  Member "staircase codes fec for 100 gb s otn":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "finite length scaling of spatially coupled ldpc codes under window decoding over the bec")
  Member "approaching capacity at high rates with iterative hard decision decoding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lattice coding for downlink multiuser transmission")

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)