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 "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.

Includes members:
  Member "capacity achieving input distributions in nondispersive optical fibers":
    also connected through other hyperedges:
      (none)
  Member "upper bound on the capacity of a cascade of nonlinear and noisy channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nonlinearity mitigation in wdm systems models strategies and achievable rates")
  Member "capacity bounds for discrete time amplitude constrained additive white gaussian noise channels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "computable upper bounds on the capacity of finite state channels")
  Member "optimal input signal distribution and per sample mutual information for nondispersive nonlinear optical fiber channel in large snr limit":
    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 "models and information rates for wiener phase noise channels")
  Member "on the per sample capacity of nondispersive optical fibers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a signal space distance measure for nondispersive optical fiber")
  Member "calculation of mutual information for partially coherent gaussian channels with applications to fiber optics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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)