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 "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.

Includes members:
  Member "lower bound on the capacity of continuous time wiener phase noise channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "capacity of discrete time wiener phase noise channels to within a constant gap")
  Member "upper bound on the capacity of discrete time wiener phase noise channels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "capacity of discrete time wiener phase noise channels to within a constant gap")
  Member "oversampling increases the pre log of noncoherent rayleigh fading channels":
    also connected through other hyperedges:
      (none)
  Member "on continuous time white phase noise channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "capacity outer bound and degrees of freedom of wiener phase noise channels with oversampling")
  Member "phase modulation for discrete time wiener phase noise channels with oversampling at high snr":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "capacity outer bound and degrees of freedom of wiener phase noise channels with oversampling")
  Member "capacity bounds for mimo microwave backhaul links affected by phase noise":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an approximate ml detector for mimo channels corrupted by phase noise")
  Member "multi sample receivers increase information rates for wiener phase noise channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "capacity outer bound and degrees of freedom of wiener phase noise channels with oversampling")
  Member "on wiener phase noise channels at high signal to noise ratio":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "capacity outer bound and degrees of freedom of wiener phase noise channels with oversampling")

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)