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: "linguistic data mining with complex networks a stylometric oriented approach"
  Abstract: Abstract   By representing a text by a set of words and their co-occurrences, one obtains a word-adjacency network being a reduced representation of a given language sample. In this paper, the possibility of using network representation to extract information about individual language styles of literary texts is studied. By determining selected quantitative characteristics of the networks and applying machine learning algorithms, it is possible to distinguish between texts of different authors. Within the studied set of texts, English and Polish, a properly rescaled weighted clustering coefficients and weighted degrees of only a few nodes in the word-adjacency networks are sufficient to obtain the authorship attribution accuracy over 90%. A correspondence between the text authorship and the word-adjacency network structure can therefore be found. The network representation allows to distinguish individual language styles by comparing the way the authors use particular words and punctuation marks. The presented approach can be viewed as a generalization of the authorship attribution methods based on simple lexical features.  Additionally, other network parameters are studied, both local and global ones, for both the unweighted and weighted networks. Their potential to capture the writing style diversity is discussed; some differences between languages are observed.

Connected through hyperedge HE-1 (induced by source paper "using word embeddings to improve the discriminability of co occurrence text networks"):
  with members:
    - "building networks for image segmentation using particle competition and cooperation"
    - "interactive image segmentation using label propagation through complex networks"
    - "complexity entropy analysis at different levels of organisation in written language"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "word sense disambiguation a complex network approach"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "in narrative texts punctuation marks obey the same statistics as words"
    - "labelled network subgraphs reveal stylistic subtleties in written texts"

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)