human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "predicting election trends with twitter hillary clinton versus donald trump"):
  Abstract: Forecasting opinion trends from real-time social media is the long-standing goal of modern-day big-data analytics. Despite its importance, there has been no conclusive scientific evidence so far that social media activity can capture the opinion of the general population at large. Here we develop analytic tools combining statistical physics of complex networks, percolation theory, natural language processing and machine learning classification to infer the opinion of Twitter users regarding the candidates of the 2016 US Presidential Election. Using a large-scale dataset of 73 million tweets collected from June 1st to September 1st 2016, we investigate the temporal social networks formed by the interactions among millions of Twitter users. We infer the support of each user to the presidential candidates and show that the resulting Twitter trends follow the New York Times National Polling Average, which represents an aggregate of hundreds of independent traditional polls, with remarkable accuracy (r=0.9). More importantly, the Twitter opinion trend forecasts the aggregated NYT polls by 6 to 15 days, showing that Twitter can be an early warning signal of global opinion trends at the national level. Our analytics [available at kcorelab.com] unleash the power of Twitter to predict social trends from elections, brands to political movements, and at a fraction of the cost of national polls.

Includes members:
  Member "deciphering the 2016 u s presidential campaign in the twitter sphere a comparison of the trumpists and clintonists":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "who falls for online political manipulation")
  Member "content and network dynamics behind egyptian political polarization on twitter":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "quantifying polarization on twitter the kavanaugh nomination")
  Member "is the sample good enough comparing data from twitter s streaming api with twitter s firehose":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sampling on networks estimating spectral centrality measures and their impact in evaluating other relevant network measures")
  Member "a meta analysis of state of the art electoral prediction from twitter data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "measuring relative opinion from location based social media a case study of the 2016 u s presidential election")
  Member "partisan asymmetries in online political activity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "topology comparison of twitter diffusion networks effectively reveals misleading information")
  Member "twitter mood predicts the stock market":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mining social media data for biomedical signals and health related behavior")

Question: Please tell me which class the source paper 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)