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 "on the calligraphy of books"):
  Abstract: Authorship attribution is a natural language processing task that has been widely studied, often by considering small order statistics. In this paper, we explore a complex network approach to assign the authorship of texts based on their mesoscopic representation, in an attempt to capture the flow of the narrative. Indeed, as reported in this work, such an approach allowed the identification of the dominant narrative structure of the studied authors. This has been achieved due to the ability of the mesoscopic approach to take into account relationships between different, not necessarily adjacent, parts of the text, which is able to capture the story flow. The potential of the proposed approach has been illustrated through principal component analysis, a comparison with the chance baseline method, and network visualization. Such visualizations reveal individual characteristics of the authors, which can be understood as a kind of calligraphy.

Includes members:
  Member "authorship attribution based on life like network automata":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "enriching complex networks with word embeddings for detecting mild cognitive impairment from speech transcripts")
  Member "text authorship identified using the dynamics of word co occurrence networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "using word embeddings to improve the discriminability of co occurrence text networks")
  Member "using network science and text analytics to produce surveys in a scientific topic":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "science through wikipedia a novel representation of open knowledge through co citation networks")
  Member "mesoscopic representation of texts as complex networks":
    also connected through other hyperedges:
      (none)
  Member "topic segmentation via community detection in complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "paragraph based representation of texts a complex networks approach")
  Member "authorship attribution using word network features":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "labelled network subgraphs reveal stylistic subtleties in written texts")
  Member "scikit learn machine learning in python":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "scail classifier weights scaling for class incremental learning")

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)