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: "the untold story of the clones content agnostic factors that impact youtube video popularity"
  Abstract: Video dissemination through sites such as YouTube can have widespread impacts on opinions, thoughts, and cultures. Not all videos will reach the same popularity and have the same impact. Popularity differences arise not only because of differences in video content, but also because of other "content-agnostic" factors. The latter factors are of considerable interest but it has been difficult to accurately study them. For example, videos uploaded by users with large social networks may tend to be more popular because they tend to have more interesting content, not because social network size has a substantial direct impact on popularity.   In this paper, we develop and apply a methodology that is able to accurately assess, both qualitatively and quantitatively, the impacts of various content-agnostic factors on video popularity. When controlling for video content, we observe a strong linear "rich-get-richer" behavior, with the total number of previous views as the most important factor except for very young videos. The second most important factor is found to be video age. We analyze a number of phenomena that may contribute to rich-get-richer, including the first-mover advantage, and search bias towards popular videos. For young videos we find that factors other than the total number of previous views, such as uploader characteristics and number of keywords, become relatively more important. Our findings also confirm that inaccurate conclusions can be reached when not controlling for content.

Connected through hyperedge HE-1 (induced by source paper "identifying the sentiment styles of youtube s vloggers"):
  with members:
    - "fever a large scale dataset for fact extraction and verification"
    - "automatic detection of fake news"
    - "towards end to end speech recognition with deep convolutional neural networks"
    - "the emotional arcs of stories are dominated by six basic shapes"
    - "very deep convolutional networks for end to end speech recognition"

Connected through hyperedge HE-2 (induced by source paper "sunstar a cost effective multi server solution for reliable video delivery"):
  with members:
    - "msplayer multi source and multi path leveraged youtuber"

Connected through hyperedge HE-3 (induced by source paper "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"):
  with members:
    - "time matters multi scale temporalization of social media popularity"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "item popularity prediction in e commerce using image quality feature vectors"
    - "beyond exchangeability the chinese voting process"
    - "learning distributed representations of sentences from unlabelled data"
    - "understanding image virality"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "predictability of popularity gaps between prediction and understanding"):
  with members:
    - "distinguishing between personal preferences and social influence in online activity feeds"
    - "studying and modeling the connection between people s preferences and content sharing"
    - "the simple rules of social contagion"
    - "from micro to macro uncovering and predicting information cascading process with behavioral dynamics"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "can cascades be predicted"
    - "virality prediction and community structure in social networks"
    - "the pulse of news in social media forecasting popularity"

Connected through hyperedge HE-5 (induced by source paper "do cascades recur"):
  with members:
    - "information evolution in social networks"
    - "the bursty dynamics of the twitter information network"
    - "can cascades be predicted"
    - "the role of social networks in information diffusion"
    - "information diffusion and external influence in networks"
    - "trends in social media persistence and decay"

Connected through hyperedge HE-6 (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:
    - "who watches and shares what on youtube and when using twitter to understand youtube viewership"
    - "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-7 (induced by source paper "deep feelings a massive cross lingual study on the relation between emotions and virality"):
  with members:
    - "depechemood a lexicon for emotion analysis from crowd annotated news"
    - "anger is more influential than joy sentiment correlation in weibo"
    - "the role of emotions in propagating brands in social networks"
    - "how to ask for a favor a case study on the success of altruistic requests"
    - "the effect of wording on message propagation topic and author controlled natural experiments on twitter"
    - "exploring image virality in google plus"
    - "exploring text virality in social networks"
    - "how the scientific community reacts to newly submitted preprints article downloads twitter mentions and citations"

Connected through hyperedge HE-8 (induced by source paper "all who wander on the prevalence and characteristics of multi community engagement"):
  with members:
    - "evolution of reddit from the front page of the internet to a self referential community"
    - "how community feedback shapes user behavior"
    - "exploring text virality in social networks"
    - "do linguistic style and readability of scientific abstracts affect their virality"
    - "you had me at hello how phrasing affects memorability"
    - "good friends bad news affect and virality in twitter"
    - "on the interplay between social and topical structure"
    - "mark my words linguistic style accommodation in social media"

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)