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: "comparing the topological properties of real and artificially generated scientific manuscripts"
  Abstract: Recent years have witnessed the increase of competition in science. While promoting the quality of research in many cases, an intense competition among scientists can also trigger unethical scientific behaviors. To increase the total number of published papers, some authors even resort to software tools that are able to produce grammatical, but meaningless scientific manuscripts. Because automatically generated papers can be misunderstood as real papers, it becomes of paramount importance to develop means to identify these scientific frauds. In this paper, I devise a methodology to distinguish real manuscripts from those generated with SCIGen, an automatic paper generator. Upon modeling texts as complex networks (CN), it was possible to discriminate real from fake papers with at least 89 % of accuracy. A systematic analysis of features relevance revealed that the accessibility and betweenness were useful in particular cases, even though the relevance depended upon the dataset. The successful application of the methods described here show, as a proof of principle, that network features can be used to identify scientific gibberish papers. In addition, the CN-based approach can be combined in a straightforward fashion with traditional statistical language processing methods to improve the performance in identifying artificially generated papers.

Connected through hyperedge HE-1 (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"
    - "text authorship identified using the dynamics of word co occurrence networks"

Connected through hyperedge HE-2 (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"
    - "a complex network approach to stylometry"

Connected through hyperedge HE-3 (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-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"
    - "a complex network approach to stylometry"
    - "probing the topological properties of complex networks modeling short written texts"
    - "inducing language networks from continuous space word representations"

Connected through hyperedge HE-5 (induced by source paper "connecting network science and information theory"):
  with members:
    - "knowledge acquisition a complex networks approach"
    - "using network science and text analytics to produce surveys in a scientific topic"
    - "causal network inference by optimal causation entropy"
    - "probing the topological properties of complex networks modeling short written texts"

Connected through hyperedge HE-6 (induced by source paper "word sense disambiguation a complex network approach"):
  with members:
    - "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 through function word adjacency networks"
    - "a systematic comparison of supervised classifiers"
    - "discriminating word senses with tourist walks in complex networks"
    - "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 "the dynamics of knowledge acquisition via self learning in complex networks"):
  with members:
    - "heterogeneous information network model for equipment standard system"
    - "knowledge acquisition a complex networks approach"
    - "using network science and text analytics to produce surveys in a scientific topic"
    - "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"
    - "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 multilayer networks"):
  with members:
    - "using network science and text analytics to produce surveys in a scientific topic"
    - "concentric network symmetry grasps authors styles in word adjacency networks"
    - "authorship recognition via fluctuation analysis of network topology and word intermittency"
    - "a complex network approach to stylometry"
    - "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"

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)