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: "interference focusing for mitigating cross phase modulation in a simplified optical fiber model"
  Abstract: A memoryless interference network model is introduced that is based on non-linear phenomena observed when transmitting information over optical fiber using wavelength division multiplexing. The main characteristic of the model is that amplitude variations on one carrier wave are converted to phase variations on another carrier wave, i.e., the carriers interfere with each other through amplitude-to-phase conversion. For the case of two carriers, a new technique called interference focusing is proposed where each carrier achieves the capacity pre-log 1, thereby doubling the pre-log of 1/2 achieved by using conventional methods. The technique requires neither channel time variations nor global channel state information. Generalizations to more than two carriers are outlined.

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"
    - "upper bound on the capacity of discrete 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"

Connected through hyperedge HE-2 (induced by source paper "models and information rates for multiuser optical fiber channels with nonlinearity and dispersion"):
  with members:
    - "real interference alignment exploiting the potential of single antenna systems"
    - "capacity of a nonlinear optical channel with finite memory"
    - "information transmission using the nonlinear fourier transform part ii numerical methods"
    - "information transmission using the nonlinear fourier transform part i mathematical tools"
    - "information transmission using the nonlinear fourier transform part iii spectrum modulation"
    - "interference focusing for simplified optical fiber models with dispersion"
    - "on the per sample capacity of nondispersive optical fibers"

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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "phase modulation for discrete time wiener phase noise channels with oversampling at high snr"):
  with members:
    - "on continuous time white phase noise channels"
    - "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"

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)