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: "weighted information and entropy rates"
  Abstract: The weighted entropy $H^{\rm w}_\phi (X)=H^{\rm w}_\phi (f)$ of a random variable $X$ with values $x$ and a probability-mass/density function $f$ is defined as the mean value ${\mathbb E} I^{\rm w}_\phi(X)$ of the weighted information $I^{\rm w}_\phi (x)=-\phi (x)\log\,f(x)$. Here $x\mapsto\phi (x)\in{\mathbb R}$ is a given weight function (WF) indicating a 'value' of outcome $x$. For an $n$-component random vector ${\mathbf{X}}_0^{n-1}=(X_0,\ldots ,X_{n-1})$ produced by a random process ${\mathbf{X}}=(X_i,i\in{\mathbb Z})$, the weighted information $I^{\rm w}_{\phi_n}({\mathbf x}_0^{n-1})$ and weighted entropy $H^{\rm w}_{\phi_n}({\mathbf{X}}_0^{n-1})$ are defined similarly, with an WF $\phi_n({\mathbf x}_0^{n-1})$. Two types of WFs $\phi_n$ are considered, based on additive and a multiplicative forms ($\phi_n({\mathbf x}_0^{n-1})=\sum\limits_{i=0}^{n-1}{\varphi} (x_i)$ and $\phi_n({\mathbf x}_0^{n-1})=\prod\limits_{i=0}^{n-1}{\varphi} (x_i)$, respectively). The focus is upon ${\it rates}$ of the weighted entropy and information, regarded as parameters related to ${\mathbf{X}}$. We show that, in the context of ergodicity, a natural scale for an asymptotically additive/multiplicative WF is $\frac{1}{n^2}H^{\rm w}_{\phi_n}({\mathbf{X}}_0^{n-1})$ and $\frac{1}{n}\log\;H^{\rm w}_{\phi_n}({\mathbf{X}}_0^{n-1})$, respectively. This gives rise to ${\it primary}$ ${\it rates}$. The next-order terms can also be identified, leading to ${\it secondary}$ ${\it rates}$. We also consider emerging generalisations of the Shannon-McMillan-Breiman theorem.

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)