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: "concentric network symmetry grasps authors styles in word adjacency networks"
  Abstract: Several characteristics of written texts have been inferred from statistical analysis derived from networked models. Even though many network measurements have been adapted to study textual properties at several levels of complexity, some textual aspects have been disregarded. In this paper, we study the symmetry of word adjacency networks, a well-known representation of text as a graph. A statistical analysis of the symmetry distribution performed in several novels showed that most of the words do not display symmetric patterns of connectivity. More specifically, the merged symmetry displayed a distribution similar to the ubiquitous power-law distribution. Our experiments also revealed that the studied metrics do not correlate with other traditional network measurements, such as the degree or the betweenness centrality. The discriminability power of the symmetry measurements was verified in the authorship attribution task. Interestingly, we found that specific authors prefer particular types of symmetric motifs. As a consequence, the authorship of books could be accurately identified in 82.5% of the cases, in a dataset comprising books written by 8 authors. Because the proposed measurements for text analysis are complementary to the traditional approach, they can be used to improve the characterization of text networks, which might be useful for applications based on stylistic classification.

Connected through hyperedge HE-1 (induced by source paper "using word embeddings to improve the discriminability of co occurrence text networks"):
  with members:
    - "building networks for image segmentation using particle competition and cooperation"
    - "interactive image segmentation using label propagation through complex networks"
    - "complexity entropy analysis at different levels of organisation in written language"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "linguistic data mining with complex networks a stylometric oriented approach"
    - "word sense disambiguation a complex network approach"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "in narrative texts punctuation marks obey the same statistics as words"

Connected through hyperedge HE-2 (induced by source paper "comparing the impact of subfields in scientific journals"):
  with members:
    - "practical method to reclassify web of science articles into unique subject categories and broad disciplines"
    - "recency predicts bursts in the evolution of author citations"
    - "patterns of authors contribution in scientific manuscripts"
    - "knowledge acquisition a complex networks approach"
    - "citation success index an intuitive pair wise journal comparison metric"
    - "a review of the literature on citation impact indicators"
    - "citations versus journal impact factor as proxy of quality could the latter ever be preferable"
    - "indicators of the interdisciplinarity of journals diversity centrality and citations"

Connected through hyperedge HE-3 (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"
    - "using complex networks for text classification discriminating informative and imaginative documents"
    - "topic segmentation via community detection in complex networks"
    - "a complex network approach to stylometry"
    - "probing the topological properties of complex networks modeling short written texts"

Connected through hyperedge HE-4 (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"
    - "using complex networks for text classification discriminating informative and imaginative documents"

Connected through hyperedge HE-5 (induced by source paper "an image analysis approach to text analytics based on complex networks"):
  with members:
    - "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"
    - "a complex network approach to stylometry"
    - "probing the topological properties of complex networks modeling short written texts"
    - "authorship attribution through function word adjacency networks"
    - "structure semantics interplay in complex networks and its effects on the predictability of similarity in texts"

Connected through hyperedge HE-6 (induced by source paper "extractive multi document summarization using multilayer networks"):
  with members:
    - "using network science and text analytics to produce surveys in a scientific topic"
    - "authorship recognition via fluctuation analysis of network topology and word intermittency"
    - "a complex network approach to stylometry"
    - "comparing the topological properties of real and artificially generated scientific manuscripts"
    - "structure semantics interplay in complex networks and its effects on the predictability of similarity in texts"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-7 (induced by source paper "authorship attribution based on life like network automata"):
  with members:
    - "authorship recognition via fluctuation analysis of network topology and word intermittency"
    - "a complex network approach to stylometry"
    - "authorship attribution through function word adjacency networks"
    - "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-8 (induced by source paper "extractive multi document summarization using dynamical measurements of complex networks"):
  with members:
    - "authorship recognition via fluctuation analysis of network topology and word intermittency"
    - "distributed representations of words and phrases and their compositionality"

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)