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: "mining missing hyperlinks from human navigation traces a case study of wikipedia"
  Abstract: Hyperlinks are an essential feature of the World Wide Web. They are especially important for online encyclopedias such as Wikipedia: an article can often only be understood in the context of related articles, and hyperlinks make it easy to explore this context. But important links are often missing, and several methods have been proposed to alleviate this problem by learning a linking model based on the structure of the existing links. Here we propose a novel approach to identifying missing links in Wikipedia. We build on the fact that the ultimate purpose of Wikipedia links is to aid navigation. Rather than merely suggesting new links that are in tune with the structure of existing links, our method finds missing links that would immediately enhance Wikipedia's navigability. We leverage data sets of navigation paths collected through a Wikipedia-based human-computation game in which users must find a short path from a start to a target article by only clicking links encountered along the way. We harness human navigational traces to identify a set of candidates for missing links and then rank these candidates. Experiments show that our procedure identifies missing links of high quality.

Connected through hyperedge HE-1 (induced by source paper "quantifying engagement with citations on wikipedia"):
  with members:
    - "citation needed a taxonomy and algorithmic assessment of wikipedia s verifiability"
    - "mapping the backbone of the humanities through the eyes of wikipedia"
    - "detecting and gauging impact on wikipedia page views"
    - "eliciting new wikipedia users interests via automatically mined questionnaires for a warm welcome not a cold start"
    - "why the world reads wikipedia beyond english speakers"
    - "structuring wikipedia articles with section recommendations"
    - "amplifying the impact of open access wikipedia and the diffusion of science"
    - "why we read wikipedia"

Connected through hyperedge HE-2 (induced by source paper "wikilinkgraphs a complete longitudinal and multi language dataset of the wikipedia link networks"):
  with members:
    - "benchmarking distributed stream processing engines"
    - "growing wikipedia across languages via recommendation"
    - "societal controversies in wikipedia articles"
    - "interactions of cultures and top people of wikipedia from ranking of 24 language editions"
    - "biographical social networks on wikipedia a cross cultural study of links that made history"
    - "ranking of wikipedia articles in search engines revisited fair ranking for reasonable quality"
    - "pagerank standing on the shoulders of giants"

Connected through hyperedge HE-3 (induced by source paper "a graph structured dataset for wikipedia research"):
  with members:
    - "spatio temporal data mining a survey of problems and methods"
    - "using triangles to improve community detection in directed networks"

Connected through hyperedge HE-4 (induced by source paper "what makes a link successful on wikipedia"):
  with members:
    - "mixedtrails bayesian hypothesis comparison on heterogeneous sequential data"
    - "improving website hyperlink structure using server logs"
    - "hyptrails a bayesian approach for comparing hypotheses about human trails on the web"
    - "detecting memory and structure in human navigation patterns using markov chain models of varying order"

Connected through hyperedge HE-5 (induced by source paper "improving website hyperlink structure using server logs"):
  with members:
    - "hyptrails a bayesian approach for comparing hypotheses about human trails on the web"
    - "user session identification based on strong regularities in inter activity time"

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)