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: "models and information rates for wiener phase noise channels"
  Abstract: A waveform channel is considered where the transmitted signal is corrupted by Wiener phase noise and additive white Gaussian noise. A discrete-time channel model that considers the effect of filtering on the phase noise is developed. The model is based on a multi-sample receiver, i.e., an integrate-and-dump filter whose output is sampled at a rate higher than the signaling rate. It is shown that, at high Signal-to-Noise Ratio (SNR), the multi-sample receiver achieves a rate that grows logarithmically with the SNR if the number of samples per symbol (oversampling factor) grows with the cubic root of the SNR. Moreover, the pre-log factor is at least 1/2 and can be achieved by amplitude modulation. For an approximate discrete-time model of the multi-sample receiver, the capacity pre-log at high SNR is shown to be at least 3/4 if the number of samples per symbol grows with the square root of the SNR. The analysis shows that phase modulation achieves a pre-log of at least 1/4, while amplitude modulation still achieves a pre-log of 1/2. This is strictly greater than the capacity pre-log of the (approximate) discrete-time Wiener phase noise channel with only one sample per symbol, which is 1/2. Numerical simulations are used to compute lower bounds on the information rates achieved by the multi-sample receiver. The simulations show that oversampling is beneficial for both strong and weak phase noise at high SNR. In fact, the information rates are sometimes substantially larger than when using commonly-used approximate discrete-time models.

Connected through hyperedge HE-1 (induced by source paper "capacity outer bound and degrees of freedom of wiener phase noise channels with oversampling"):
  with members:
    - "capacity of discrete time wiener phase noise channels to within a constant gap"
    - "on the capacity of the wiener phase noise channel bounds and capacity achieving distributions"
    - "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"
    - "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"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-2 (induced by source paper "an approximate ml detector for mimo channels corrupted by phase noise"):
  with members:
    - "capacity bounds for mimo microwave backhaul links affected by phase noise"
    - "constellation optimization in the presence of strong 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 multiplexing gain of discrete time mimo phase noise channels"):
  with members:
    - "capacity of simo and miso phase noise channels with common separate oscillators"
    - "capacity bounds for mimo microwave backhaul links affected by phase noise"
    - "on wiener phase noise channels at high signal to noise ratio"

Connected through hyperedge HE-4 (induced by source paper "lower bound on the capacity of continuous time wiener phase noise channels"):
  with members:
    - "upper bound on the capacity of discrete time 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"

Connected through hyperedge HE-5 (induced by source paper "channel estimation carrier recovery and data detection in the presence of phase noise in ofdm relay systems"):
  with members:
    - "analysis of oscillator phase noise effects on self interference cancellation in full duplex ofdm radio transceivers"
    - "channel phase noise and frequency offset in ofdm systems joint estimation data detection and hybrid cramer rao lower bound"
    - "joint source and relay precoding designs for mimo two way relaying based on mse criterion"

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)