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 "authorship attribution based on life like network automata"):
  Abstract: The authorship attribution is a problem of considerable practical and technical interest. Several methods have been designed to infer the authorship of disputed documents in multiple contexts. While traditional statistical methods based solely on word counts and related measurements have provided a simple, yet effective solution in particular cases; they are prone to manipulation. Recently, texts have been successfully modeled as networks, where words are represented by nodes linked according to textual similarity measurements. Such models are useful to identify informative topological patterns for the authorship recognition task. However, there is no consensus on which measurements should be used. Thus, we proposed a novel method to characterize text networks, by considering both topological and dynamical aspects of networks. Using concepts and methods from cellular automata theory, we devised a strategy to grasp informative spatio-temporal patterns from this model. Our experiments revealed an outperformance over traditional analysis relying only on topological measurements. Remarkably, we have found a dependence of pre-processing steps (such as the lemmatization) on the obtained results, a feature that has mostly been disregarded in related works. The optimized results obtained here pave the way for a better characterization of textual networks.

Includes members:
  Member "concentric network symmetry grasps authors styles in word adjacency 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 "authorship recognition via fluctuation analysis of network topology and word intermittency":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (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-4 (induced by source paper "edgenets edge varying graph neural networks")
  Member "a systematic comparison of supervised classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (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)