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: "variation in the vocabulary of russian literary texts"
  Abstract: In this paper, the data from a large online collection is used to study variation in the vocabulary of Russian literary texts. First, we find that variation in the vocabulary size of different authors is in a good agreement with Heaps' law with parameter $\lambda=0.5$, consistent with previous studies. The overall distribution of word frequencies has lighter tails than both the Zipf and lognormal laws predict. Next, we focus on the variation of word frequencies across texts. We confirm statistically that word frequencies vary significantly across texts, and find that the variance of the cross-text frequency distribution is in general higher for more frequent words. The dependence of the variance on the average word frequency follows a power law with exponent $\alpha=1.25.$ The factor models applied to the data suggest that the most of the cross-text variation is concentrated in less than $100$ most frequent words and can be explained by about $10$ factors. For less-frequent words, the frequency data is more suitable for factor analysis if it is normalized to take into account the size of texts. The matrix of normalized frequencies for less-frequent words exhibit properties characteristic for a large random matrix deformed by a low-rank matrix. As an example of application, the spectral factors for the most frequent words are used to classify texts, and it is found that the k-means classification algorithm based on these factors can classify texts by authors with the accuracy that varied from about $70%$ to $90%$ for prose writers, and from $60%$ to $80%$ for poets.

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.CL(Computation and Language)