human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "labelled network subgraphs reveal stylistic subtleties in written texts"):
  Abstract: The vast amount of data and increase of computational capacity have allowed the analysis of texts from several perspectives, including the representation of texts as complex networks. Nodes of the network represent the words, and edges represent some relationship, usually word co-occurrence. Even though networked representations have been applied to study some tasks, such approaches are not usually combined with traditional models relying upon statistical paradigms. Because networked models are able to grasp textual patterns, we devised a hybrid classifier, called labelled subgraphs, that combines the frequency of common words with small structures found in the topology of the network, known as motifs. Our approach is illustrated in two contexts, authorship attribution and translationese identification. In the former, a set of novels written by different authors is analyzed. To identify translationese, texts from the Canadian Hansard and the European parliament were classified as to original and translated instances. Our results suggest that labelled subgraphs are able to represent texts and it should be further explored in other tasks, such as the analysis of text complexity, language proficiency, and machine translation.

Includes members:
  Member "on the modeling of musical solos as complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the complex network structure of musical pieces analysis of some use cases from different music genres")
  Member "using complex networks for text classification discriminating informative and imaginative documents":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "paragraph based representation of texts a complex networks approach")
  Member "authorship attribution via network motifs identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using word embeddings to improve the discriminability of co occurrence text networks")
  Member "authorship recognition via fluctuation analysis of network topology and word intermittency":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "representation of texts as complex networks a mesoscopic approach")
  Member "a complex network approach to stylometry":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using word embeddings to improve the discriminability of co occurrence text networks")
  Member "probing the topological properties of complex networks modeling short written texts":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using word embeddings to improve the discriminability of co occurrence text networks")
  Member "authorship attribution using word network features":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the calligraphy of books")
  Member "structure semantics interplay in complex networks and its effects on the predictability of similarity in texts":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "representation of texts as complex networks a mesoscopic approach")

Question: Please tell me which class the source paper 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)