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 network science and text analytics to produce surveys in a scientific topic"
  Abstract: The use of science to understand its own structure is becoming popular, but understanding the organization of knowledge areas is still limited because some patterns are only discoverable with proper computational treatment of large-scale datasets. In this paper, we introduce a framework to combine network-based methodologies and text analytics to construct the taxonomy of science fields. The methodology is illustrated with application to two topics: complex networks (CN) and photonic crystals (PC). We built citation networks using data from the Web of Science and used a community detection algorithm for partitioning to obtain science maps for the two topics. We also created an importance index for text analytics, which is employed to extract keywords that define the communities and, combined with network topology metrics, to generate dendrograms of relatedness among subtopics. Interesting patterns emerging from the analysis included identification of two well-defined communities in PC area, which is consistent with the known existence of two distinct communities of researchers in the area: telecommunication engineers and physicists. With the methodology, it was also possible to assess the interdisciplinary nature and time evolution of subtopics defined by the keywords. The automatic tools described here are potentially useful not only to provide an overview of scientific areas but also to assist scientists in performing systematic research on a specific topic.

Connected through hyperedge HE-1 (induced by source paper "science through wikipedia a novel representation of open knowledge through co citation networks"):
  with members:
    - "mapping the backbone of the humanities through the eyes of wikipedia"
    - "the insoluble problems of books what does altmetric com have to offer"
    - "amplifying the impact of open access wikipedia and the diffusion of science"
    - "knowledge acquisition a complex networks approach"
    - "using almetrics for contextualised mapping of societal impact from hits to networks"
    - "power laws in citation distributions evidence from scopus"
    - "mapping citation patterns of book chapters in the book citation index"
    - "global maps of science based on the new web of science categories"

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 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"
    - "probing the topological properties of complex networks modeling short written texts"

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 complex networks for text classification discriminating informative and imaginative documents"
    - "cohesion and coalition formation in the european parliament roll call votes and twitter activities"

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"
    - "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 "connecting network science and information theory"):
  with members:
    - "knowledge acquisition a complex networks approach"
    - "causal network inference by optimal causation entropy"
    - "probing the topological properties of complex networks modeling short written texts"
    - "comparing the topological properties of real and artificially generated scientific manuscripts"

Connected through hyperedge HE-6 (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"
    - "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"
    - "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-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"
    - "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"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "extractive multi document summarization using multilayer networks"):
  with members:
    - "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"
    - "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"

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.SI(Social and Information Networks)