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: "a complex network approach to stylometry"
  Abstract: Statistical methods have been widely employed to study the fundamental properties of language. In recent years, methods from complex and dynamical systems proved useful to create several language models. Despite the large amount of studies devoted to represent texts with physical models, only a limited number of studies have shown how the properties of the underlying physical systems can be employed to improve the performance of natural language processing tasks. In this paper, I address this problem by devising complex networks methods that are able to improve the performance of current statistical methods. Using a fuzzy classification strategy, I show that the topological properties extracted from texts complement the traditional textual description. In several cases, the performance obtained with hybrid approaches outperformed the results obtained when only traditional or networked methods were used. Because the proposed model is generic, the framework devised here could be straightforwardly used to study similar textual applications where the topology plays a pivotal role in the description of the interacting agents.

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 "text mining forma mentis networks reconstruct public perception of the stem gender gap in social media"):
  with members:
    - "from topic networks to distributed cognitive maps zipfian topic universes in the area of volunteered geographic information"
    - "investigating italian disinformation spreading on twitter in the context of 2019 european elections"
    - "historical comparison of gender inequality in scientific careers across countries and disciplines"
    - "validation of twitter opinion trends with national polling aggregates hillary clinton vs donald trump"
    - "text authorship identified using the dynamics of word co occurrence networks"
    - "probing the topological properties of complex networks modeling short written texts"

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"
    - "concentric network symmetry grasps authors styles in word adjacency networks"
    - "probing the topological properties of complex networks modeling short written texts"

Connected through hyperedge HE-4 (induced by source paper "word sense induction using word embeddings and community detection in complex networks"):
  with members:
    - "word sense disambiguation a complex network approach"
    - "text authorship identified using the dynamics of word co occurrence networks"
    - "robust clustering of languages across wikipedia growth"
    - "using network science and text analytics to produce surveys in a scientific topic"
    - "clustering algorithms a comparative approach"
    - "probing the topological properties of complex networks modeling short written texts"
    - "comparing the topological properties of real and artificially generated scientific manuscripts"
    - "inducing language networks from continuous space word representations"

Connected through hyperedge HE-5 (induced by source paper "a linear complexity multi biometric forensic document analysis system by fusing the stylome and signature modalities"):
  with members:
    - "authorship attribution through function word adjacency networks"
    - "on the computational efficiency of training neural networks"
    - "complexity of word collocation networks a preliminary structural analysis"

Connected through hyperedge HE-6 (induced by source paper "complexity entropy analysis at different levels of organisation in written language"):
  with members:
    - "probing the topological properties of complex networks modeling short written texts"

Connected through hyperedge HE-7 (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"
    - "using complex networks for text classification discriminating informative and imaginative documents"
    - "authorship recognition via fluctuation analysis of network topology and word intermittency"
    - "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-8 (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"
    - "concentric network symmetry grasps authors styles in word adjacency networks"
    - "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"

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)