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: "who watches and shares what on youtube and when using twitter to understand youtube viewership"
  Abstract: By combining multiple social media datasets, it is possible to gain insight into each dataset that goes beyond what could be obtained with either individually. In this paper we combine user-centric data from Twitter with video-centric data from YouTube to build a rich picture of who watches and shares what on YouTube. We study 87K Twitter users, 5.6 million YouTube videos and 15 million video sharing events from user-, video- and sharing-event-centric perspectives. We show that features of Twitter users correlate with YouTube features and sharing-related features. For example, urban users are quicker to share than rural users. We find a superlinear relationship between initial Twitter shares and the final amounts of views. We discover that Twitter activity metrics play more role in video popularity than mere amount of followers. We also reveal the existence of correlated behavior concerning the time between video creation and sharing within certain timescales, showing the time onset for a coherent response, and the time limit after which collective responses are extremely unlikely. Response times depend on the category of the video, suggesting Twitter video sharing is highly dependent on the video content. To the best of our knowledge, this is the first large-scale study combining YouTube and Twitter data, and it reveals novel, detailed insights into who watches (and shares) what on YouTube, and when.

Connected through hyperedge HE-1 (induced by source paper "estimating attention flow in online video networks"):
  with members:
    - "offline evaluation of ranking policies with click models"
    - "top k off policy correction for a reinforce recommender system"
    - "sir hawkes linking epidemic models and hawkes processes to model diffusions in finite populations"
    - "foundations of sequence to sequence modeling for time series"
    - "beyond views measuring and predicting engagement on youtube videos"
    - "online popularity under promotion viral potential forecasting and the economics of time"
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "do cascades recur"

Connected through hyperedge HE-2 (induced by source paper "analysis of the influence of internet tv station on wikipedia page views"):
  with members:
    - "wikipedia page view reflects web search trend"

Connected through hyperedge HE-3 (induced by source paper "beyond views measuring and predicting engagement on youtube videos"):
  with members:
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "a data driven study of view duration on youtube"
    - "feature driven and point process approaches for popularity prediction"
    - "exploring limits to prediction in complex social systems"
    - "understanding the detection of view fraud in video content portals"
    - "trendlearner early prediction of popularity trends of user generated content"
    - "can cascades be predicted"
    - "fake view analytics in online video services"

Connected through hyperedge HE-4 (induced by source paper "discovery retrieval and analysis of star wars botnet in twitter"):
  with members:
    - "dissecting a social botnet growth content and influence in twitter"
    - "the darpa twitter bot challenge"
    - "political bots and the manipulation of public opinion in venezuela"
    - "reverse engineering socialbot infiltration strategies in twitter"
    - "truthy mapping the spread of astroturf in microblog streams"

Connected through hyperedge HE-5 (induced by source paper "online popularity under promotion viral potential forecasting and the economics of time"):
  with members:
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "smart broadcasting do you want to be seen"
    - "when to post on social networks"

Connected through hyperedge HE-6 (induced by source paper "the qwerty effect on the web how typing shapes the meaning of words in online human computer interaction"):
  with members:
    - "inferring networks of substitutable and complementary products"
    - "image based recommendations on styles and substitutes"
    - "sentiment cascades in the 15m movement"
    - "social signals and algorithmic trading of bitcoin"
    - "gender asymmetries in reality and fiction the bechdel test of social media"
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"
    - "positive words carry less information than negative words"
    - "happiness is assortative in online social networks"

Connected through hyperedge HE-7 (induced by source paper "prediction of video popularity in the absence of reliable data from video hosting services utility of traces left by users on the web"):
  with members:
    - "the untold story of the clones content agnostic factors that impact youtube video popularity"
    - "social dynamics of digg"
    - "using a model of social dynamics to predict popularity of news"
    - "predicting the popularity of online content"

Connected through hyperedge HE-8 (induced by source paper "robust contextual outlier detection where context meets sparsity"):
  with members:
    - "sequential hypothesis tests for adaptive locality sensitive hashing"

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)