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: "deciphering the 2016 u s presidential campaign in the twitter sphere a comparison of the trumpists and clintonists"
  Abstract: In this paper, we study follower demographics of Donald Trump and Hillary Clinton, the two leading candidates in the 2016 U.S. presidential race. We build a unique dataset US2016, which includes the number of followers for each candidate from September 17, 2015 to December 22, 2015. US2016 also includes the geographical location of these followers, the number of their own followers and, very importantly, the profile image of each follower. We use individuals' number of followers and profile images to analyze four dimensions of follower demographics: social status, gender, race and age. Our study shows that in terms of social influence, the Trumpists are more polarized than the Clintonists: they tend to have either a lot of influence or little influence. We also find that compared with the Clintonists, the Trumpists are more likely to be either very young or very old. Our study finds no gender affinity effect for Clinton in the Twitter sphere, but we do find that the Clintonists are more racially diverse.

Connected through hyperedge HE-1 (induced by source paper "who falls for online political manipulation"):
  with members:
    - "arming the public with artificial intelligence to counter social bots"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "early detection of promoted campaigns on social media"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "botornot a system to evaluate social bots"
    - "the darpa twitter bot challenge"
    - "the rise of social bots"

Connected through hyperedge HE-2 (induced by source paper "validation of twitter opinion trends with national polling aggregates hillary clinton vs donald trump"):
  with members:
    - "online human bot interactions detection estimation and characterization"
    - "the effects of twitter sentiment on stock price returns"
    - "content and network dynamics behind egyptian political polarization on twitter"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"
    - "a meta analysis of state of the art electoral prediction from twitter data"
    - "partisan asymmetries in online political activity"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-3 (induced by source paper "the great division"):
  with members:
    - "detection and analysis of 2016 us presidential election related rumors on twitter"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"
    - "pricing the woman card gender politics between hillary clinton and donald trump"
    - "voting with feet who are leaving hillary clinton and donald trump"

Connected through hyperedge HE-4 (induced by source paper "how polarized have we become a multimodal classification of trump followers and clinton followers"):
  with members:
    - "a selfie is worth a thousand words mining personal patterns behind user selfie posting behaviours"
    - "catching fire via likes inferring topic preferences of trump followers on twitter"
    - "using word embeddings in twitter election classification"
    - "do they all look the same deciphering chinese japanese and koreans by fine grained deep learning"
    - "gender politics in the 2016 u s presidential election a computer vision approach"
    - "actionable and political text classification using word embeddings and lstm"
    - "pricing the woman card gender politics between hillary clinton and donald trump"
    - "voting with feet who are leaving hillary clinton and donald trump"

Connected through hyperedge HE-5 (induced by source paper "protest activity detection and perceived violence estimation from social media images"):
  with members:
    - "greysanatomy vs yankees demographics and hashtag use on twitter"
    - "deep residual learning for image recognition"
    - "characterizing the demographics behind the blacklivesmatter movement"
    - "deep learning face attributes in the wild"
    - "6 seconds of sound and vision creativity in micro videos"
    - "big questions for social media big data representativeness validity and other methodological pitfalls"

Connected through hyperedge HE-6 (induced by source paper "when follow is just one click away understanding twitter follow behavior in the 2016 u s presidential election"):
  with members:
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "multi view face detection using deep convolutional neural networks"
    - "deep residual learning for image recognition"
    - "a century of portraits a visual historical record of american high school yearbooks"

Connected through hyperedge HE-7 (induced by source paper "tactics and tallies a study of the 2016 u s presidential campaign using twitter likes"):
  with members:
    - "to follow or not to follow analyzing the growth patterns of the trumpists on twitter"

Connected through hyperedge HE-8 (induced by source paper "trump vs hillary what went viral during the 2016 us presidential election"):
  with members:
    - "botornot a system to evaluate social bots"

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)