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: "the asymptotic capacity of the optical fiber"
  Abstract: It is shown that signal energy is the only available degree-of-freedom (DOF) for fiber-optic transmission as the input power tends to infinity. With $n$ signal DOFs at the input, $n-1$ DOFs are asymptotically lost to signal-noise interactions. The main observation is that, nonlinearity introduces a multiplicative noise in the channel, similar to fading in wireless channels. The channel is viewed in the spherical coordinate system, where signal vector $\underline{X}\in\mathbb{C}^n$ is represented in terms of its norm $|\underline{X}|$ and direction $\underline{\hat{X}}$. The multiplicative noise causes signal direction $\underline{\hat{X}}$ to vary randomly on the surface of the unit $(2n-1)$-sphere in $\mathbb{C}^{n}$, in such a way that the effective area of the support of $\underline{\hat{X}}$ does not vanish as $|\underline{X}|\rightarrow\infty$. On the other hand, the surface area of the sphere is finite, so that $\underline{\hat{X}}$ carries finite information. This observation is used to show several results. Firstly, let $\mathcal C(\mathcal P)$ be the capacity of a discrete-time periodic model of the optical fiber with distributed noise and frequency-dependent loss, as a function of the average input power $\mathcal P$. It is shown that asymptotically as $\mathcal P\rightarrow\infty$, $\mathcal C=\frac{1}{n}\log\bigl(\log\mathcal P\bigr)+c$, where $n$ is the dimension of the input signal space and $c$ is a bounded number. In particular, $\lim_{\mathcal P\rightarrow\infty}\mathcal C(\mathcal P)=\infty$ in finite-dimensional periodic models. Secondly, it is shown that capacity saturates to a constant in infinite-dimensional models where $n=\infty$.

Connected through hyperedge HE-1 (induced by source paper "capacity achieving input distributions in nondispersive optical fibers"):
  with members:
    - "on properties of the support of capacity achieving distributions for additive noise channel models with input cost constraints"
    - "on the finiteness of the capacity of continuous channels"
    - "upper bound on the capacity of a cascade of nonlinear and noisy channels"
    - "information theoretic proofs of entropy power inequalities"
    - "the noncoherent rician fading channel part i structure of the capacity achieving input"

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)