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: "mutual information as a figure of merit for optical fiber systems"
  Abstract: Advanced channel decoders rely on soft-decision decoder inputs for which mutual information (MI) is the natural figure of merit. In this paper, we analyze an optical fiber system by evaluating MI as the maximum achievable rate of transmission of such a system. MI is estimated by means of histograms for which the correct bin number is determined in a blind way. The MI estimate obtained this way shows excellent accuracy in comparison with the true MI of 16-state quadrature amplitude modulation (QAM) over an additive white Gaussian noise channel with additional phase noise, which is a simplified model of a nonlinear optical fiber channel. We thereby justify to use the MI estimation method to accurately estimate the MI of an optical fiber system. In the second part of this work, a transoceanic fiber system with 6000 km of standard single-mode fiber is simulated and its MI determined. Among rectangular QAMs, 16-QAM is found to be the optimal modulation scheme for this link as to performance in terms of MI and requirements on components and digital signal processing. For the reported MI of 3.1 bits/symbol, a minimum coding overhead of 29% is required when the channel memory is not taken into account. By employing ideal single-channel digital back-propagation, an increase in MI by 0.25 bits/symbol and 0.28 bits/symbol is reported for 16-QAM and 64-QAM, respectively, lowering the required overhead to 19% and 16%. When the channel spacing is decreased to be close to the Nyquist rate, the dual-polarization spectral efficiency is 5.7 bits/s/Hz, an increase of more than 2 bits/symbol compared to a 50 GHz spacing.

Connected through hyperedge HE-1 (induced by source paper "non parametric estimation of mutual information with application to nonlinear optical fibers"):
  with members:
    - "improved lower bounds on mutual information accounting for nonlinear signal noise interaction"
    - "a non parametric k nearest neighbour entropy estimator"
    - "upper bound on the capacity of a cascade of nonlinear and noisy channels"
    - "capacity of a nonlinear optical channel with finite memory"

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)