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: "upper bound on the capacity of discrete time wiener phase noise channels"
  Abstract: A discrete-time Wiener phase noise channel with an integrate-and-dump multi-sample receiver is studied. An upper bound to the capacity with an average input power constraint is derived, and a high signal-to-noise ratio (SNR) analysis is performed. If the oversampling factor grows as SNRα for 0 ≤ α ≤ 1, then the capacity pre-log is at most (1 + α)/2 at high SNR.

Connected through hyperedge HE-1 (induced by source paper "models and information rates for wiener phase noise channels"):
  with members:
    - "lower bound on the capacity of continuous time wiener phase noise channels"
    - "oversampling increases the pre log of noncoherent rayleigh fading channels"
    - "on continuous time white phase noise channels"
    - "phase modulation for discrete time wiener phase noise channels with oversampling at high snr"
    - "capacity bounds for mimo microwave backhaul links affected by phase noise"
    - "multi sample receivers increase information rates for wiener phase noise channels"
    - "on wiener phase noise channels at high signal to noise ratio"
    - "calculation of mutual information for partially coherent gaussian channels with applications to fiber optics"

Connected through hyperedge HE-2 (induced by source paper "capacity of discrete time wiener phase noise channels to within a constant gap"):
  with members:
    - "on the capacity of the wiener phase noise channel bounds and capacity achieving distributions"
    - "lower bound on the capacity of continuous time wiener phase noise channels"
    - "phase modulation for discrete time wiener phase noise channels with oversampling at high snr"
    - "on wiener phase noise channels at high signal to noise ratio"

Connected through hyperedge HE-3 (induced by source paper "lower bound on the capacity of continuous time wiener phase noise channels"):
  with members:
    - "models and information rates for wiener phase noise channels"
    - "on continuous time white phase noise channels"
    - "phase modulation for discrete time wiener phase noise channels with oversampling at high snr"
    - "multi sample receivers increase information rates for wiener phase noise channels"
    - "tight upper and lower bounds to the information rate of the phase noise channel"
    - "on wiener phase noise channels at high signal to noise ratio"
    - "interference focusing for mitigating cross phase modulation in a simplified optical fiber model"

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)