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: "design of apsk constellations for coherent optical channels with nonlinear phase noise"
  Abstract: We study the design of amplitude phase-shift keying (APSK) constellations for a coherent fiber-optical communication system where nonlinear phase noise (NLPN) is the main system impairment. APSK constellations can be regarded as a union of phase-shift keying (PSK) signal sets with different amplitude levels. A practical two-stage (TS) detection scheme is analyzed, which performs close to optimal detection for high enough input power. We optimize APSK constellations with 4, 8, and 16 points in terms of symbol error probability (SEP) under TS detection for several combinations of input power and fiber length. For 16 points, performance gains of 3.2 dB can be achieved at a SEP of 10-2 compared to 16-QAM by choosing an optimized APSK constellation. We also demonstrate that in the presence of severe nonlinear distortions, it may become beneficial to sacrifice a constellation point or an entire constellation ring to reduce the average SEP. Finally, we discuss the problem of selecting a good binary labeling for the found constellations.

Connected through hyperedge HE-1 (induced by source paper "a signal space distance measure for nondispersive optical fiber"):
  with members:
    - "towards a non stochastic information theory"
    - "autocorrelation function for dispersion free fiber channels with distributed amplification"
    - "fast numerical nonlinear fourier transforms"
    - "upper bound on the capacity of the nonlinear schr odinger channel"
    - "information transmission using the nonlinear fourier transform part iii spectrum modulation"
    - "a nonstochastic information theory for communication and state estimation"
    - "optimizing constellations for single subcarrier intensity modulated optical systems"
    - "on the per sample capacity of nondispersive optical fibers"

Connected through hyperedge HE-2 (induced by source paper "convolutionally coded snr adaptive transmission for low latency communications"):
  with members:
    - "a survey on low latency towards 5g ran core network and caching solutions"
    - "variable length convolutional coding for short blocklengths with decision feedback"

Connected through hyperedge HE-3 (induced by source paper "achievable information rates for nonlinear fiber communication via end to end autoencoder learning"):
  with members:
    - "end to end deep learning of optical fiber communications"
    - "an introduction to deep learning for the physical layer"
    - "nonlinear interference mitigation via deep neural networks"
    - "on achievable rates for long haul fiber optic communications"
    - "on the per sample capacity of nondispersive optical fibers"

Connected through hyperedge HE-4 (induced by source paper "demodulation and detection schemes for a memoryless optical wdm channel"):
  with members:
    - "information transmission using the nonlinear fourier transform part iii spectrum modulation"
    - "interference focusing for simplified optical fiber models with dispersion"
    - "on the per sample capacity of nondispersive optical fibers"

Connected through hyperedge HE-5 (induced by source paper "four dimensional coded modulation with bit wise decoders for future optical communications"):
  with members:
    - "high snr asymptotics of mutual information for discrete constellations with applications to bicm"
    - "improving soft fec performance for higher order modulations via optimized bit channel mappings"
    - "probabilistic signal shaping for bit metric decoding"
    - "signal shaping for bicm at low snr"
    - "general ber expression for one dimensional constellations"
    - "a pragmatic coded modulation scheme for high spectral efficiency fiber optic communications"
    - "calculation of mutual information for partially coherent gaussian channels with applications to fiber optics"
    - "optimal alphabets and binary labelings for bicm at low snr"

Connected through hyperedge HE-6 (induced by source paper "constellation optimization in the presence of strong phase noise"):
  with members:
    - "soft metrics and their performance analysis for optimal data detection in the presence of strong oscillator phase noise"
    - "constellation design for channels affected by phase noise"
    - "optimal alphabets and binary labelings for bicm at low snr"

Connected through hyperedge HE-7 (induced by source paper "a low complexity detector for memoryless polarization multiplexed fiber optical channels"):
  with members:
    - "on the per sample capacity of nondispersive optical fibers"

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)