human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "structure and inference in annotated networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on a scalable entropic breaching of the overfitting barrier in machine learning")
  Member "a complex network approach to stylometry":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "using word embeddings to improve the discriminability of co occurrence text networks")
  Member "comparing the topological properties of real and artificially generated scientific manuscripts":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semantic flow in language networks")
  Member "aggregated journal journal citation relations in scopus and web of science matched and compared in terms of networks maps and interactive overlays":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discontinuities in citation relations among journals self organized criticality as a model of scientific revolutions and change")
  Member "citnetexplorer a new software tool for analyzing and visualizing citation networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a principled methodology for comparing relatedness measures for clustering publications")
  Member "interactive overlays of journals and the measurement of interdisciplinarity on the basis of aggregated journal journal citations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "early detection of research trends")
  Member "pagerank for ranking authors in co citation networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "assigning credit to scientific datasets using article citation networks")
  Member "lexrank graph based lexical centrality as salience in text summarization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generating representative headlines for news stories")

Question: Please tell me which class the source paper 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)