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: "why we read wikipedia"
  Abstract: Wikipedia is one of the most popular sites on the Web, with millions of users relying on it to satisfy a broad range of information needs every day. Although it is crucial to understand what exactly these needs are in order to be able to meet them, little is currently known about why users visit Wikipedia. The goal of this paper is to fill this gap by combining a survey of Wikipedia readers with a log-based analysis of user activity. Based on an initial series of user surveys, we build a taxonomy of Wikipedia use cases along several dimensions, capturing users' motivations to visit Wikipedia, the depth of knowledge they are seeking, and their knowledge of the topic of interest prior to visiting Wikipedia. Then, we quantify the prevalence of these use cases via a large-scale user survey conducted on live Wikipedia with almost 30,000 responses. Our analyses highlight the variety of factors driving users to Wikipedia, such as current events, media coverage of a topic, personal curiosity, work or school assignments, or boredom. Finally, we match survey responses to the respondents' digital traces in Wikipedia's server logs, enabling the discovery of behavioral patterns associated with specific use cases. For instance, we observe long and fast-paced page sequences across topics for users who are bored or exploring randomly, whereas those using Wikipedia for work or school spend more time on individual articles focused on topics such as science. Our findings advance our understanding of reader motivations and behavior on Wikipedia and can have implications for developers aiming to improve Wikipedia's user experience, editors striving to cater to their readers' needs, third-party services (such as search engines) providing access to Wikipedia content, and researchers aiming to build tools such as recommendation engines.

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"
    - "growing wikipedia across languages via recommendation"

Connected through hyperedge HE-2 (induced by source paper "what is trending on wikipedia capturing trends and language biases across wikipedia editions"):
  with members:
    - "a graph structured dataset for wikipedia research"
    - "why the world reads wikipedia beyond english speakers"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "analysing timelines of national histories across wikipedia editions a comparative computational approach"
    - "mining cross cultural relations from wikipedia a study of 31 european food cultures"

Connected through hyperedge HE-3 (induced by source paper "wikihist html english wikipedia s full revision history in html format"):
  with members:
    - "wikilinkgraphs a complete longitudinal and multi language dataset of the wikipedia link networks"
    - "why the world reads wikipedia beyond english speakers"
    - "what makes a link successful on wikipedia"

Connected through hyperedge HE-4 (induced by source paper "why the world reads wikipedia beyond english speakers"):
  with members:
    - "analysing timelines of national histories across wikipedia editions a comparative computational approach"
    - "what makes a link successful on wikipedia"
    - "measuring urban deprivation from user generated content"
    - "cultures in community question answering"
    - "you are what you eat and drink identifying cultural boundaries by analyzing food drink habits in foursquare"
    - "detecting memory and structure in human navigation patterns using markov chain models of varying order"
    - "mining cross cultural relations from wikipedia a study of 31 european food cultures"

Connected through hyperedge HE-5 (induced by source paper "query for architecture click through military comparing the roles of search and navigation on wikipedia"):
  with members:
    - "toktrack a complete token provenance and change tracking dataset for the english wikipedia"
    - "analysing timelines of national histories across wikipedia editions a comparative computational approach"
    - "improving website hyperlink structure using server logs"
    - "what makes a link successful on wikipedia"
    - "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"

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)