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: "a divergence formula for randomness and dimension"
  Abstract: If $S$ is an infinite sequence over a finite alphabet $\Sigma$ and $\beta$ is a probability measure on $\Sigma$, then the {\it dimension} of $ S$ with respect to $\beta$, written $\dim^\beta(S)$, is a constructive version of Billingsley dimension that coincides with the (constructive Hausdorff) dimension $\dim(S)$ when $\beta$ is the uniform probability measure. This paper shows that $\dim^\beta(S)$ and its dual $\Dim^\beta(S)$, the {\it strong dimension} of $S$ with respect to $\beta$, can be used in conjunction with randomness to measure the similarity of two probability measures $\alpha$ and $\beta$ on $\Sigma$. Specifically, we prove that the {\it divergence formula} \[ #R##N#\dim^\beta(R) = \Dim^\beta(R) =\frac{\CH(\alpha)}{\CH(\alpha) + \D(\alpha || \beta)} \] holds whenever $\alpha$ and $\beta$ are computable, positive probability measures on $\Sigma$ and $R \in \Sigma^\infty$ is random with respect to $\alpha$. In this formula, $\CH(\alpha)$ is the Shannon entropy of $\alpha$, and $\D(\alpha||\beta)$ is the Kullback-Leibler divergence between $\alpha$ and $\beta$. We also show that the above formula holds for all sequences $R$ that are $\alpha$-normal (in the sense of Borel) when $\dim^\beta(R)$ and $\Dim^\beta(R)$ are replaced by the more effective finite-state dimensions $\dimfs^\beta(R)$ and $\Dimfs^\beta(R)$. In the course of proving this, we also prove finite-state compression characterizations of $\dimfs^\beta(S)$ and $\Dimfs^\beta(S)$.

Connected through hyperedge HE-1 (induced by source paper "asymptotic divergences and strong dichotomy"):
  with members:
    - "automatic kolmogorov complexity and normality revisited"
    - "computing absolutely normal numbers in nearly linear time"
    - "dimensions of copeland erdos sequences"
    - "dimension in complexity classes"
    - "the dimensions of individual strings and sequences"

Connected through hyperedge HE-2 (induced by source paper "mutual dimension and random sequences"):
  with members:
    - "analysis of boolean functions"
    - "dimension in complexity classes"
    - "the dimensions of individual strings and sequences"

Connected through hyperedge HE-3 (induced by source paper "a divergence formula for randomness and dimension short version"):
  with members:
    - "dimension in complexity classes"
    - "the dimensions of individual strings and sequences"

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)