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 role of words in the network structure of texts application to authorship attribution"):
  Abstract: Abstract   Well-established automatic analyses of texts mainly consider frequencies of linguistic units, e.g. letters, words, and bigrams. In a recent, alternative approach, medium and large-scale text structures were used in opposition to the belief that text structure is dominated by the language features. In this paper, we introduce a generalized similarity measure to compare texts which accounts for both the network structure of texts and the role of individual words in the networks. The similarity measure is used for authorship attribution of three collections of books, each composed of 8 authors and 10 books per author. High accuracy rates were obtained with typical values between    90  %    and    98  .  75  %   , much higher than with the traditional term frequency-inverse document frequency (tf-idf) approach for the same collections. These accuracies are also higher than those obtained solely with the topology of networks. We conclude that the different properties of specific words on the macroscopic scale structure of a whole text are as relevant as their frequency of appearance; conversely, considering the identity of nodes brings further knowledge about a piece of text represented as a network.

Includes members:
  Member "text authorship identified using the dynamics of word co occurrence networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "using word embeddings to improve the discriminability of co occurrence text networks")
  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 recognition via fluctuation analysis of network topology and word intermittency":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "representation of texts as complex networks a mesoscopic approach")
  Member "probing the topological properties of complex networks modeling short written texts":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using word embeddings to improve the discriminability of co occurrence text networks")
  Member "authorship attribution through function word adjacency networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "edgenets edge varying graph neural networks")
  Member "stability and continuity of centrality measures in weighted graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sampling on networks estimating spectral centrality measures and their impact in evaluating other relevant network measures")
  Member "a systematic comparison of supervised classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "paragraph based representation of texts a complex networks approach")
  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)