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: "what makes a link successful on wikipedia"
  Abstract: While a plethora of hypertext links exist on the Web, only a small amount of them are regularly clicked. Starting from this observation, we set out to study large-scale click data from Wikipedia in order to understand what makes a link successful. We systematically analyze effects of link properties on the popularity of links. By utilizing mixed-effects hurdle models supplemented with descriptive insights, we find evidence of user preference towards links leading to the periphery of the network, towards links leading to semantically similar articles, and towards links in the top and left-side of the screen. We integrate these findings as Bayesian priors into a navigational Markov chain model and by doing so successfully improve the model fits. We further adapt and improve the well-known classic PageRank algorithm that assumes random navigation by accounting for observed navigational preferences of users in a weighted variation. This work facilitates understanding navigational click behavior and thus can contribute to improving link structures and algorithms utilizing these structures.

Connected through hyperedge HE-1 (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"
    - "why we read wikipedia"

Connected through hyperedge HE-2 (induced by source paper "quantifying the impact of user attention on fair group representation in ranked lists"):
  with members:
    - "fairness in online social network timelines measurements models and mechanism design"
    - "equity of attention amortizing individual fairness in rankings"
    - "fairness of exposure in rankings"
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "ranking with fairness constraints"
    - "fa ir a fair top k ranking algorithm"
    - "unbiased learning to rank with biased feedback"
    - "measuring fairness in ranked outputs"

Connected through hyperedge HE-3 (induced by source paper "why the world reads wikipedia beyond english speakers"):
  with members:
    - "why we read wikipedia"
    - "analysing timelines of national histories across wikipedia editions a comparative computational approach"
    - "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-4 (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"
    - "why we read wikipedia"
    - "analysing timelines of national histories across wikipedia editions a comparative computational approach"
    - "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 "mixedtrails bayesian hypothesis comparison on heterogeneous sequential data"):
  with members:
    - "tribeflow mining predicting user trajectories"
    - "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)