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: "preliminary report on the structure of croatian linguistic co occurrence networks"
  Abstract: In this article, we investigate the structure of Croatian linguistic co-occurrence networks. We examine the change of network structure properties by systematically varying the co-occurrence window sizes, the corpus sizes and removing stopwords. In a co-occurrence window of size $n$ we establish a link between the current word and $n-1$ subsequent words. The results point out that the increase of the co-occurrence window size is followed by a decrease in diameter, average path shortening and expectedly condensing the average clustering coefficient. The same can be noticed for the removal of the stopwords. Finally, since the size of texts is reflected in the network properties, our results suggest that the corpus influence can be reduced by increasing the co-occurrence window size.

Connected through hyperedge HE-1 (induced by source paper "multilayer network of language a unified framework for structural analysis of linguistic subsystems"):
  with members:
    - "comparison of the language networks from literature and blogs"
    - "toward selectivity based keyword extraction for croatian news"

Connected through hyperedge HE-2 (induced by source paper "toward network based keyword extraction from multitopic web documents"):
  with members:
    - "keyword and keyphrase extraction using centrality measures on collocation networks"
    - "learning algorithms for keyphrase extraction"
    - "kea practical automatic keyphrase extraction"

Connected through hyperedge HE-3 (induced by source paper "toward selectivity based keyword extraction for croatian news"):
  with members:
    - "comparison of the language networks from literature and blogs"
    - "keyword and keyphrase extraction using centrality measures on collocation networks"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-4 (induced by source paper "comparison of the language networks from literature and blogs"):
  with members:
    - "a preliminary study of croatian language syllable networks"
    - "initial comparison of linguistic networks measures for parallel texts"

Connected through hyperedge HE-5 (induced by source paper "complex networks measures for differentiation between normal and shuffled croatian texts"):
  with members:
    - "initial comparison of linguistic networks measures for parallel texts"
    - "the effect of linguistic constraints on the large scale organization of language"

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)