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: "wikipedia traffic data and electoral prediction towards theoretically informed models"
  Abstract: This aim of this article is to explore the potential use of Wikipedia page view data for predicting electoral results. Responding to previous critiques of work using socially generated data to predict elections, which have argued that these predictions take place without any understanding of the mechanism which enables them, we first develop a theoretical model which highlights why people might seek information online at election time, and how this activity might relate to overall electoral outcomes, focussing especially on information seeking incentives related to swing voters and new parties. We test this model on a novel dataset drawn from a variety of countries in the 2009 and 2014 European Parliament elections. We show that while Wikipedia offers little insight into absolute vote outcomes, it does offer good information about changes in overall turnout at elections and about changes in vote share for particular parties. These results are used to enhance existing theories about the drivers of aggregate patterns in online information seeking, by suggesting that voters are cognitive misers who seek information only when considering changing their vote.

Connected through hyperedge HE-1 (induced by source paper "novel version of pagerank cheirank and 2drank for wikipedia in multilingual network using social impact"):
  with members:
    - "world influence and interactions of universities from wikipedia networks"
    - "hoprank how semantic structure influences teleportation in pagerank a case study on bioportal"
    - "wikipedia ranking of world universities"
    - "interactions of cultures and top people of wikipedia from ranking of 24 language editions"
    - "spectral properties of google matrix of wikipedia and other networks"
    - "toward two dimensional search engines"
    - "two dimensional ranking of wikipedia articles"

Connected through hyperedge HE-2 (induced by source paper "big data would not lie prediction of the 2016 taiwan election via online heterogeneous information"):
  with members:
    - "140 characters to victory using twitter to predict the uk 2015 general election"
    - "paying for likes understanding facebook like fraud using honeypots"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-3 (induced by source paper "computational international relations what can programming coding and internet research do for the discipline"):
  with members:
    - "explaining the emergence of echo chambers on social media the role of ideology and extremism"
    - "the spatial ecology of war and peace"

Connected through hyperedge HE-4 (induced by source paper "personal vs know how contacts which matter more in wiki elections"):
  with members:
    - "140 characters to victory using twitter to predict the uk 2015 general election"
    - "predicting links in ego networks using temporal information"

Connected through hyperedge HE-5 (induced by source paper "does campaigning on social media make a difference evidence from candidate use of twitter during the 2015 and 2017 uk elections"):
  with members:
    - "where in the world are you geolocation and language identification in twitter"
    - "a meta analysis of state of the art electoral prediction from twitter data"

Connected through hyperedge HE-6 (induced by source paper "parsimonious data how a single facebook like predicts voting behavior in multiparty systems"):
  with members:
    - "predicting links in ego networks using temporal information"

Connected through hyperedge HE-7 (induced by source paper "estimating local commuting patterns from geolocated twitter data"):
  with members:
    - "predicting human mobility through the assimilation of social media traces into mobility models"
    - "geo located twitter as proxy for global mobility patterns"
    - "uncovering patterns of inter urban trip and spatial interaction from social media check in data"
    - "where in the world are you geolocation and language identification in twitter"
    - "multi scale population and mobility estimation with geo tagged tweets"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"

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)