human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "using complex networks for text classification discriminating informative and imaginative documents"
  Abstract: Statistical methods have been widely employed in recent years to grasp many language properties. The application of such techniques have allowed an improvement of several linguistic applications, such as machine translation and document classification. In the latter, many approaches have emphasised the semantical content of texts, as is the case of bag-of-word language models. These approaches have certainly yielded reasonable performance. However, some potential features such as the structural organization of texts have been used only in a few studies. In this context, we probe how features derived from textual structure analysis can be effectively employed in a classification task. More specifically, we performed a supervised classification aiming at discriminating informative from imaginative documents. Using a networked model that describes the local topological/dynamical properties of function words, we achieved an accuracy rate of up to 95%, which is much higher than similar networked approaches. A systematic analysis of feature relevance revealed that symmetry and accessibility measurements are among the most prominent network measurements. Our results suggest that these measurements could be used in related language applications, as they play a complementary role in characterising texts.

Connected through hyperedge HE-1 (induced by source paper "paragraph based representation of texts a complex networks approach"):
  with members:
    - "an image analysis approach to text analytics based on complex networks"
    - "representation of texts as complex networks a mesoscopic approach"
    - "on the calligraphy of books"
    - "using network science and text analytics to produce surveys in a scientific topic"
    - "topic segmentation via community detection in complex networks"
    - "concentric network symmetry grasps authors styles in word adjacency networks"
    - "a complex network approach to stylometry"
    - "probing the topological properties of complex networks modeling short written texts"

Connected through hyperedge HE-2 (induced by source paper "a complex network approach to political analysis application to the brazilian chamber of deputies"):
  with members:
    - "from louvain to leiden guaranteeing well connected communities"
    - "word sense disambiguation a complex network approach"
    - "extractive multi document summarization using multilayer networks"
    - "analyzing dynamic ideological communities in congressional voting networks"
    - "patterns of authors contribution in scientific manuscripts"
    - "knowledge acquisition a complex networks approach"
    - "using network science and text analytics to produce surveys in a scientific topic"
    - "cohesion and coalition formation in the european parliament roll call votes and twitter activities"

Connected through hyperedge HE-3 (induced by source paper "representation of texts as complex networks a mesoscopic approach"):
  with members:
    - "the emotional arcs of stories are dominated by six basic shapes"
    - "authorship recognition via fluctuation analysis of network topology and word intermittency"
    - "a complex network approach to stylometry"
    - "modeling the average shortest path length in growth of word adjacency networks"
    - "authorship attribution through function word adjacency networks"
    - "word length entropies and correlations of natural language written texts"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "on the role of words in the network structure of texts application to authorship attribution"):
  with members:
    - "text authorship identified using the dynamics of word co occurrence networks"
    - "authorship recognition via fluctuation analysis of network topology and word intermittency"
    - "probing the topological properties of complex networks modeling short written texts"
    - "authorship attribution through function word adjacency networks"
    - "stability and continuity of centrality measures in weighted graphs"
    - "a systematic comparison of supervised classifiers"
    - "structure semantics interplay in complex networks and its effects on the predictability of similarity in texts"

Connected through hyperedge HE-5 (induced by source paper "labelled network subgraphs reveal stylistic subtleties in written texts"):
  with members:
    - "on the modeling of musical solos as complex networks"
    - "authorship attribution via network motifs identification"
    - "authorship recognition via fluctuation analysis of network topology and word intermittency"
    - "a complex network approach to stylometry"
    - "probing the topological properties of complex networks modeling short written texts"
    - "authorship attribution using word network features"
    - "structure semantics interplay in complex networks and its effects on the predictability of similarity in texts"
    - "measuring the evolution of contemporary western popular music"

Question: Please tell me which class the center node 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)