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 "paragraph based representation of texts a complex networks approach"):
  Abstract: Abstract   An interesting model to represent texts as a graph (also called network) is the word adjacency (co-occurrence) representation, which is known to capture mainly syntactical features of texts. In this study, we propose a novel network model, which is based on the similarity between the content of the paragraphs of the text. By considering this representation, we characterized the networks with respect to measurements developed in the network science area. We characterized these measurements according to their properties regarding their ability to discriminate between real and shuffled texts, and to capture information regarding the content similarity of chunks of text. In order to compare the results with a more sophisticated approach, we employed a methodology based on word2vec. When comparing real and shuffled texts, the results revealed that real texts tend to have a more well-defined community structure. This characteristic can be related to the organization of subjects in real texts. The network-based measurements that were found to be able to discriminate real from shuffled texts were used as features in a classifier. As a result, the obtained accuracy was 98.72%. In order to compare with a different methodology, we used doc2vec-based features in the classifier, yielding an accuracy rate of 70.8%. The proposed network-based features were employed to analyze the Voynich manuscript, which was found to be compatible with real texts according to the considered characteristics.

Includes members:
  Member "an image analysis approach to text analytics based on complex networks":
    also connected through other hyperedges:
      (none)
  Member "representation of texts as complex networks a mesoscopic approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semantic flow in language networks")
  Member "on the calligraphy of books":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an image analysis approach to text analytics based on complex 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 "using complex networks for text classification discriminating informative and imaginative documents":
    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 "topic segmentation via community detection in complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the dynamics of knowledge acquisition via self learning in complex networks")
  Member "concentric network symmetry grasps authors styles in word adjacency 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")
  Member "a complex network approach to stylometry":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)