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 "semantic flow in language networks"):
  Abstract: In this study we propose a framework to characterize documents based on their semantic flow. The proposed framework encompasses a network-based model that connected sentences based on their semantic similarity. Semantic fields are detected using standard community detection methods. as the story unfolds, transitions between semantic fields are represent in Markov networks, which in turned are characterized via network motifs (subgraphs). Here we show that the proposed framework can be used to classify books according to their style and publication dates. Remarkably, even without a systematic optimization of parameters, philosophy and investigative books were discriminated with an accuracy rate of 92.5%. Because this model captures semantic features of texts, it could be used as an additional feature in traditional network-based models of texts that capture only syntactical/stylistic information, as it is the case of word adjacency (co-occurrence) networks.

Includes members:
  Member "connecting network science and information theory":
    also connected through other hyperedges:
      (none)
  Member "word sense induction using word embeddings and community detection in complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "russian word sense induction by clustering averaged word embeddings")
  Member "paragraph based representation of texts a complex networks approach":
    also connected through other hyperedges:
      (none)
  Member "representation of texts as complex networks a mesoscopic approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "paragraph based representation of texts a complex networks approach")
  Member "word sense disambiguation a complex network approach":
    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 "extractive multi document summarization using multilayer networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a complex network approach to political analysis application to the brazilian chamber of deputies")
  Member "labelled network subgraphs reveal stylistic subtleties in written texts":
    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 "text authorship identified using the dynamics of word co occurrence networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using word embeddings to improve the discriminability of co occurrence text networks")

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)