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: "dimension and relative frequencies"
  Abstract: We show how to calculate the finite-state dimension (equivalently, the finite-state compressibility) of a saturated sets $X$ consisting of {\em all} infinite sequences $S$ over a finite alphabet $\Sigma_m$ satisfying some given condition $P$ on the asymptotic frequencies with which various symbols from $\Sigma_m$ appear in $S$. When the condition $P$ completely specifies an empirical probability distribution $\pi$ over $\Sigma_m$, i.e., a limiting frequency of occurrence for {\em every} symbol in $\Sigma_m$, it has been known since 1949 that the Hausdorff dimension of $X$ is precisely $\CH(\pi)$, the Shannon entropy of $\pi$, and the finite-state dimension was proven to have this same value in 2001. #R##N#The saturated sets were studied by Volkmann and Cajar decades ago. It got attention again only with the recent developments in multifractal analysis by Barreira, Saussol, Schmeling, and separately Olsen. However, the powerful methods they used -- ergodic theory and multifractal analysis -- do not yield a value for the finite-state (or even computable) dimension in an obvious manner. #R##N#We give a pointwise characterization of finite-state dimensions of saturated sets. Simultaneously, we also show that their finite-state dimension and strong dimension coincide with their Hausdorff and packing dimension respectively, though the techniques we use are completely elementary. Our results automatically extend to less restrictive effective settings (e.g., constructive, computable, and polynomial-time dimensions).

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.CC(Computational Complexity)