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: "autocorrelation function for dispersion free fiber channels with distributed amplification"
  Abstract: The autocorrelation function of the output signal given the input signal is derived in closed form for dispersion-free fiber channels with distributed optical amplification (OA). The autocorrelation function is used to upper bound the output power of bandlimited or time-resolution limited receivers, and thereby to bound spectral broadening and the capacity of receivers with thermal noise. The output power scales at most as the square-root of the launch power, and thus capacity scales at most as one-half the logarithm of the launch power. The propagating signal bandwidth scales at least as the square-root of the launch power. However, in practice the OA bandwidth should exceed the signal bandwidth to compensate attenuation. Hence, there is a launch power threshold beyond which the fiber model loses practical relevance. Nevertheless, for the mathematical model an upper bound on capacity is developed when the OA bandwidth scales as the square-root of the launch power, in which case capacity scales at most as the inverse fourth root of the launch power.

Connected through hyperedge HE-1 (induced by source paper "a signal space distance measure for nondispersive optical fiber"):
  with members:
    - "towards a non stochastic information theory"
    - "fast numerical nonlinear fourier transforms"
    - "upper bound on the capacity of the nonlinear schr odinger channel"
    - "information transmission using the nonlinear fourier transform part iii spectrum modulation"
    - "design of apsk constellations for coherent optical channels with nonlinear phase noise"
    - "a nonstochastic information theory for communication and state estimation"
    - "optimizing constellations for single subcarrier intensity modulated optical systems"
    - "on the per sample capacity of nondispersive optical fibers"

Connected through hyperedge HE-2 (induced by source paper "the log log growth of channel capacity for nondispersive nonlinear optical fiber channel in intermediate power range extension of the model"):
  with members:
    - "next to leading order corrections to capacity for nondispersive nonlinear optical fiber channel in intermediate power region"
    - "calculation of mutual information for nonlinear communication channel at large signal to noise ratio"
    - "capacity of a nonlinear optical channel with finite memory"
    - "on the per sample capacity of nondispersive optical fibers"

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)