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: "modeling the average shortest path length in growth of word adjacency networks"
  Abstract: We investigate properties of evolving linguistic networks defined by the word-adjacency relation. Such networks belong to the category of networks with accelerated growth but their shortest path length appears to reveal the network size dependence of different functional form than the ones known so far. We thus compare the networks created from literary texts with their artificial substitutes based on different variants of the Dorogovtsev-Mendes model and observe that none of them is able to properly simulate the novel asymptotics of the shortest path length. Then, we identify the local chain-like linear growth induced by grammar and style as a missing element in this model and extend it by incorporating such effects. It is in this way that a satisfactory agreement with the empirical result is obtained.

Connected through hyperedge HE-1 (induced by source paper "linguistic data mining with complex networks a stylometric oriented approach"):
  with members:
    - "in narrative texts punctuation marks obey the same statistics as words"
    - "text authorship identified using the dynamics of word co occurrence networks"

Connected through hyperedge HE-2 (induced by source paper "representation of texts as complex networks a mesoscopic approach"):
  with members:
    - "the emotional arcs of stories are dominated by six basic shapes"
    - "using complex networks for text classification discriminating informative and imaginative documents"
    - "authorship recognition via fluctuation analysis of network topology and word intermittency"
    - "a complex network approach to stylometry"
    - "authorship attribution through function word adjacency networks"
    - "word length entropies and correlations of natural language written texts"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "robustness of sentence length measures in written texts"):
  with members:
    - "in narrative texts punctuation marks obey the same statistics as words"
    - "compression and the origins of zipf s law for word frequencies"
    - "text mixing shapes the anatomy of rank frequency distributions"
    - "punctuation effects in english and esperanto texts"
    - "beyond word frequency bursts lulls and scaling in the temporal distributions of words"

Connected through hyperedge HE-4 (induced by source paper "predicting language diversity with complex networks"):
  with members:
    - "authorship recognition via fluctuation analysis of network topology and word intermittency"
    - "a complex network approach to stylometry"
    - "diffusion of lexical change in social media"
    - "structure semantics interplay in complex networks and its effects on the predictability of similarity in texts"

Connected through hyperedge HE-5 (induced by source paper "in narrative texts punctuation marks obey the same statistics as words"):
  with members:
    - "a complex network approach to stylometry"
    - "structure semantics interplay in complex networks and its effects on the predictability of similarity in texts"
    - "linguistic complexity english vs polish text vs corpus"
    - "punctuation effects in english and esperanto texts"
    - "beyond word frequency bursts lulls and scaling in the temporal distributions of words"

Connected through hyperedge HE-6 (induced by source paper "text authorship identified using the dynamics of word co occurrence networks"):
  with members:
    - "authorship recognition via fluctuation analysis of network topology and word intermittency"
    - "a complex network approach to stylometry"
    - "probing the topological properties of complex networks modeling short written texts"
    - "a systematic comparison of supervised classifiers"
    - "a stronger null hypothesis for crossing dependencies"

Connected through hyperedge HE-7 (induced by source paper "a complex network approach to stylometry"):
  with members:
    - "authorship recognition via fluctuation analysis of network topology and word intermittency"
    - "probing the topological properties of complex networks modeling short written texts"
    - "a systematic comparison of supervised classifiers"
    - "statistical keyword detection in literary corpora"

Connected through hyperedge HE-8 (induced by source paper "quantifying origin and character of long range correlations in narrative texts"):
  with members:
    - "beyond word frequency bursts lulls and scaling in the temporal distributions of words"

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)