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: "constellation optimization in the presence of strong phase noise"
  Abstract: In this paper, we address the problem of optimizing signal constellations for strong phase noise. The problem is investigated by considering three optimization formulations, which provide an analytical framework for constellation design. In the first formulation, we seek to design constellations that minimize the symbol error probability (SEP) for an approximate ML detector in the presence of phase noise. In the second formulation, we optimize constellations in terms of mutual information (MI) for the effective discrete channel consisting of phase noise, additive white Gaussian noise, and the approximate ML detector. To this end, we derive the MI of this discrete channel. Finally, we optimize constellations in terms of the MI for the phase noise channel. We give two analytical characterizations of the MI of this channel, which are shown to be accurate for a wide range of signal-to-noise ratios and phase noise variances. For each formulation, we present a detailed analysis of the optimal constellations and their performance in the presence of strong phase noise. We show that the optimal constellations significantly outperform conventional constellations and those proposed in the literature in terms of SEP, error floors, and MI.

Connected through hyperedge HE-1 (induced by source paper "error rate analysis of amplitude coherent detection over rician fading channels with receiver diversity"):
  with members:
    - "wireless powered communication opportunities and challenges"

Connected through hyperedge HE-2 (induced by source paper "an approximate ml detector for mimo channels corrupted by phase noise"):
  with members:
    - "models and information rates for wiener phase noise channels"
    - "capacity bounds for mimo microwave backhaul links affected by phase noise"
    - "dmt optimality of lr aided linear decoders for a general class of channels lattice designs and system models"

Connected through hyperedge HE-3 (induced by source paper "on the capacity of the wiener phase noise channel bounds and capacity achieving distributions"):
  with members:
    - "capacity of simo and miso phase noise channels with common separate oscillators"
    - "on the impact of oscillator phase noise on the uplink performance in a massive mimo ofdm system"
    - "massive mimo systems with non ideal hardware energy efficiency estimation and capacity limits"
    - "calculation of the performance of communication systems from measured oscillator phase noise"
    - "improving bandwidth efficiency in e band communication systems"
    - "oscillator phase noise and small scale channel fading in higher frequency bands"
    - "analysis of oscillator phase noise effects on self interference cancellation in full duplex ofdm radio transceivers"
    - "on continuous time white phase noise channels"

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)