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: "text authorship identified using the dynamics of word co occurrence networks"
  Abstract: Automatic identification of authorship in disputed documents has benefited from complex network theory as this approach does not require human expertise or detailed semantic knowledge. Networks modeling entire books can be used to discriminate texts from different sources and understand network growth mechanisms, but only a few studies have probed the suitability of networks in modeling small chunks of text to grasp stylistic features. In this study, we introduce a methodology based on the dynamics of word co-occurrence networks representing written texts to classify a corpus of 80 texts by 8 authors. The texts were divided into sections with equal number of linguistic tokens, from which time series were created for 12 topological metrics. Since 73% of all series were stationary (ARIMA(p, 0, q)) and the remaining were integrable of first order (ARIMA(p, 1, q)), probability distributions could be obtained for the global network metrics. The metrics exhibit bell-shaped non-Gaussian distributions, and therefore distribution moments were used as learning attributes. With an optimized supervised learning procedure based on a nonlinear transformation performed by Isomap, 71 out of 80 texts were correctly classified using the K-nearest neighbors algorithm, i.e. a remarkable 88.75% author matching success rate was achieved. Hence, purely dynamic fluctuations in network metrics can characterize authorship, thus paving the way for a robust description of large texts in terms of small evolving networks.

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"
    - "a complex network approach to stylometry"
    - "probing the topological properties of complex networks modeling short written texts"

Connected through hyperedge HE-3 (induced by source paper "semantic flow in language networks"):
  with members:
    - "connecting network science and information theory"
    - "word sense induction using word embeddings and community detection in complex networks"
    - "paragraph based representation of texts a complex networks approach"
    - "representation of texts as complex networks a mesoscopic approach"
    - "word sense disambiguation a complex network approach"
    - "extractive multi document summarization using multilayer networks"
    - "labelled network subgraphs reveal stylistic subtleties in written texts"
    - "knowledge acquisition a complex networks approach"

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"
    - "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"
    - "a complex network approach to stylometry"
    - "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 "linguistic data mining with complex networks a stylometric oriented approach"):
  with members:
    - "in narrative texts punctuation marks obey the same statistics as words"
    - "modeling the average shortest path length in growth of word adjacency networks"

Connected through hyperedge HE-6 (induced by source paper "on the role of words in the network structure of texts application to authorship attribution"):
  with members:
    - "using complex networks for text classification discriminating informative and imaginative documents"
    - "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-7 (induced by source paper "on the calligraphy of books"):
  with members:
    - "authorship attribution based on life like network automata"
    - "using network science and text analytics to produce surveys in a scientific topic"
    - "mesoscopic representation of texts as complex networks"
    - "topic segmentation via community detection in complex networks"
    - "authorship attribution using word network features"
    - "scikit learn machine learning in python"

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)