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: "vaporous marketing uncovering pervasive electronic cigarette advertisements on twitter"
  Abstract: Background: Twitter has become the "wild-west" of marketing and promotional strategies for advertisement agencies. Electronic cigarettes have been heavily marketed across Twitter feeds, offering discounts, "kid-friendly" flavors, algorithmically generated false testimonials, and free samples. Methods:All electronic cigarette keyword related tweets from a 10% sample of Twitter spanning January 2012 through December 2014 (approximately 850,000 total tweets) were identified and categorized as Automated or Organic by combining a keyword classification and a machine trained Human Detection algorithm. A sentiment analysis using Hedonometrics was performed on Organic tweets to quantify the change in consumer sentiments over time. Commercialized tweets were topically categorized with key phrasal pattern matching. Results:The overwhelming majority (80%) of tweets were classified as automated or promotional in nature. The majority of these tweets were coded as commercialized (83.65% in 2013), up to 33% of which offered discounts or free samples and appeared on over a billion twitter feeds as impressions. The positivity of Organic (human) classified tweets has decreased over time (5.84 in 2013 to 5.77 in 2014) due to a relative increase in the negative words ban,tobacco,doesn't,drug,against,poison,tax and a relative decrease in the positive words like haha,good,cool. Automated tweets are more positive than organic (6.17 versus 5.84) due to a relative increase in the marketing words best,win,buy,sale,health,discount and a relative decrease in negative words like bad, hate, stupid, don't. Conclusions:Due to the youth presence on Twitter and the clinical uncertainty of the long term health complications of electronic cigarette consumption, the protection of public health warrants scrutiny and potential regulation of social media marketing.

Connected through hyperedge HE-1 (induced by source paper "deception strategies and threats for online discussions"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "anatomy of an online misinformation network"
    - "the spread of low credibility content by social bots"
    - "soc2seq social embedding meets conversation model"
    - "reuters tracer toward automated news production using large scale social media data"
    - "early detection of promoted campaigns on social media"
    - "discovery retrieval and analysis of star wars botnet in twitter"
    - "limited individual attention and online virality of low quality information"

Connected through hyperedge HE-2 (induced by source paper "deep neural networks for bot detection"):
  with members:
    - "the rise of jihadist propaganda on social networks"
    - "the spread of low credibility content by social bots"
    - "ai powered social bots"
    - "contagion dynamics of extremist propaganda in social networks"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "early detection of promoted campaigns on social media"
    - "discovery retrieval and analysis of star wars botnet in twitter"

Connected through hyperedge HE-3 (induced by source paper "early detection of promoted campaigns on social media"):
  with members:
    - "online human bot interactions detection estimation and characterization"
    - "predicting online extremism content adopters and interaction reciprocity"
    - "botornot a system to evaluate social bots"
    - "the darpa twitter bot challenge"
    - "do cascades recur"
    - "the rise of social bots"
    - "science vs conspiracy collective narratives in the age of misinformation"
    - "computational fact checking from knowledge networks"

Connected through hyperedge HE-4 (induced by source paper "online human bot interactions detection estimation and characterization"):
  with members:
    - "discovery retrieval and analysis of star wars botnet in twitter"
    - "predicting online extremism content adopters and interaction reciprocity"
    - "botivist calling volunteers to action using online bots"
    - "botornot a system to evaluate social bots"
    - "the darpa twitter bot challenge"
    - "the rise of social bots"
    - "sifting robotic from organic text a natural language approach for detecting automation on twitter"
    - "quantifying the effect of sentiment on information diffusion in social media"

Connected through hyperedge HE-5 (induced by source paper "on the influence of social bots in online protests"):
  with members:
    - "botornot a system to evaluate social bots"
    - "the rise of social bots"
    - "bots strongerin and brexit computational propaganda during the uk eu referendum"
    - "sifting robotic from organic text a natural language approach for detecting automation on twitter"
    - "the new war correspondents the rise of civic media curation in urban warfare"
    - "a meta analysis of state of the art electoral prediction from twitter data"
    - "information transfer 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)