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: "voting with feet who are leaving hillary clinton and donald trump"
  Abstract: From a crowded field with 17 candidates, Hillary Clinton and Donald Trump have emerged as the two front-runners in the 2016 U.S. presidential campaign. The two candidates each boast more than 5 million followers on Twitter, and at the same time both have witnessed hundreds of thousands of people leave their camps. In this paper we attempt to characterize individuals who have left Hillary Clinton and Donald Trump between September 2015 and March 2016. #R##N#Our study focuses on three dimensions of social demographics: social capital, gender, and age. Within each camp, we compare the characteristics of the current followers with former followers, i.e., individuals who have left since September 2015. We use the number of followers to measure social capital, and profile images to infer gender and age. For classifying gender, we train a convolutional neural network (CNN). For age, we use the Face++ API. #R##N#Our study shows that for both candidates followers with more social capital are more likely to leave (or switch camps). For both candidates females make up a larger presence among unfollowers than among current followers. Somewhat surprisingly, the effect is particularly pronounced for Clinton. Lastly, middle-aged individuals are more likely to leave Trump, and the young are more likely to leave Hillary Clinton.

Connected through hyperedge HE-1 (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"
    - "deciphering the 2016 u s presidential campaign in the twitter sphere a comparison of the trumpists and clintonists"

Connected through hyperedge HE-2 (induced by source paper "a hybrid model for role related user classification on twitter"):
  with members:
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"
    - "gender identity and lexical variation in social media"

Connected through hyperedge HE-3 (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"
    - "deciphering the 2016 u s presidential campaign in the twitter sphere a comparison of the trumpists and clintonists"

Connected through hyperedge HE-4 (induced by source paper "gender politics in the 2016 u s presidential election a computer vision approach"):
  with members:
    - "catching fire via likes inferring topic preferences of trump followers on twitter"
    - "to follow or not to follow analyzing the growth patterns of the trumpists on twitter"
    - "deciphering the 2016 u s presidential campaign in the twitter sphere a comparison of the trumpists and clintonists"
    - "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"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "will sanders supporters jump ship for trump fine grained analysis of twitter followers"):
  with members:
    - "catching fire via likes inferring topic preferences of trump followers on twitter"
    - "pricing the woman card gender politics between hillary clinton and donald trump"
    - "to follow or not to follow analyzing the growth patterns of the trumpists on twitter"
    - "deciphering the 2016 u s presidential campaign in the twitter sphere a comparison of the trumpists and clintonists"
    - "a century of portraits a visual historical record of american high school yearbooks"

Connected through hyperedge HE-6 (induced by source paper "tactics and tallies inferring voter preferences in the 2016 u s presidential primaries using sparse learning"):
  with members:
    - "catching fire via likes inferring topic preferences of trump followers on twitter"
    - "to follow or not to follow analyzing the growth patterns of the trumpists on twitter"
    - "deciphering the 2016 u s presidential campaign in the twitter sphere a comparison of the trumpists and clintonists"

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)