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: "catching fire via likes inferring topic preferences of trump followers on twitter"
  Abstract: In this paper, we propose a framework to infer the topic preferences of Donald Trump's followers on Twitter. We first use latent Dirichlet allocation (LDA) to derive the weighted mixture of topics for each Trump tweet. Then we use negative binomial regression to model the "likes," with the weights of each topic serving as explanatory variables. Our study shows that attacking Democrats such as President Obama and former Secretary of State Hillary Clinton earns Trump the most "likes." Our framework of inference is generalizable to the study of other politicians.

Connected through hyperedge HE-1 (induced by source paper "catching up with trends the changing landscape of political discussions on twitter in 2014 and 2019"):
  with members:
    - "tactics and tallies inferring voter preferences in the 2016 u s presidential primaries using sparse learning"
    - "twitter and polls analyzing and estimating political orientation of twitter users in india general elections2014"

Connected through hyperedge HE-2 (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"
    - "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"
    - "deciphering the 2016 u s presidential campaign in the twitter sphere a comparison of the trumpists and clintonists"

Connected through hyperedge HE-3 (induced by source paper "latent dirichlet allocation lda and topic modeling models applications a survey"):
  with members:
    - "improving topic models with latent feature word representations"
    - "fast online em for big topic modeling"
    - "sub story detection in twitter with hierarchical dirichlet processes"
    - "unveiling the political agenda of the european parliament plenary a topical analysis"
    - "et lda joint topic modeling for aligning events and their twitter feedback"
    - "expectation propogation for the generative aspect model"
    - "the author topic model for authors and documents"
    - "modeling online reviews with multi grain topic models"

Connected through hyperedge HE-4 (induced by source paper "detection and analysis of 2016 us presidential election related rumors on twitter"):
  with members:
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "gender politics in the 2016 u s presidential election a computer vision approach"):
  with members:
    - "voting with feet who are leaving 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"
    - "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-6 (induced by source paper "will sanders supporters jump ship for trump fine grained analysis of twitter followers"):
  with members:
    - "pricing the woman card gender politics between hillary clinton and donald trump"
    - "voting with feet who are leaving 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-7 (induced by source paper "tactics and tallies inferring voter preferences in the 2016 u s presidential primaries using sparse learning"):
  with members:
    - "voting with feet who are leaving 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"

Connected through hyperedge HE-8 (induced by source paper "pricing the woman card gender politics between hillary clinton and donald trump"):
  with members:
    - "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"

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)