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 "lower bound on the capacity of continuous time wiener phase noise channels"):
  Abstract: A continuous-time Wiener phase noise channel with an integrate-and-dump multi-sample receiver is studied. #R##N#A lower bound to the capacity with an average input power constraint is derived, and a high signal-to-noise ratio (SNR) analysis is performed. #R##N#The capacity pre-log depends on the oversampling factor, and amplitude and phase modulation do not equally contribute to capacity at high SNR.

Includes members:
  Member "models and information rates for wiener phase noise channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "capacity outer bound and degrees of freedom of wiener phase noise channels with oversampling")
  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 "models and information rates for wiener phase noise channels")
  Member "on continuous time white phase noise channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "models and information rates for wiener phase noise channels")
  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 "models and information rates for wiener phase noise channels")
  Member "multi sample receivers increase information rates for wiener phase noise channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "models and information rates for wiener phase noise channels")
  Member "tight upper and lower bounds to the information rate of the phase noise channel":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "upper bound on the capacity of discrete time wiener phase noise channels")
  Member "on wiener phase noise channels at high signal to noise ratio":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "models and information rates for wiener phase noise channels")
  Member "interference focusing for mitigating cross phase modulation in a simplified optical fiber model":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "models and information rates for wiener phase noise channels")

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)