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 "validation of twitter opinion trends with national polling aggregates hillary clinton vs donald trump"):
  Abstract: Measuring and forecasting opinion trends from real-time social media is a long-standing goal of big-data analytics. Despite the large amount of work addressing this question, there has been no clear validation of online social media opinion trend with traditional surveys. Here we develop a method to infer the opinion of Twitter users by using a combination of statistical physics of complex networks and machine learning based on hashtags co-occurrence to build an in-domain training set of the order of a million tweets. We validate our method in the context of 2016 US Presidential Election by comparing the Twitter opinion trend with the New York Times National Polling Average, representing an aggregate of hundreds of independent traditional polls. The Twitter opinion trend follows the aggregated NYT polls with remarkable accuracy. We investigate the dynamics of the social network formed by the interactions among millions of Twitter supporters and infer the support of each user to the presidential candidates. Our analytics unleash the power of Twitter to uncover social trends from elections, brands to political movements, and at a fraction of the cost of traditional surveys.

Includes members:
  Member "online human bot interactions detection estimation and characterization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning")
  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-2 (induced by source paper "who falls for online political manipulation")
  Member "the effects of twitter sentiment on stock price returns":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cashtag piggybacking uncovering spam and bot activity in stock microblogs on twitter")
  Member "content and network dynamics behind egyptian political polarization on twitter":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (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-6 (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-7 (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-8 (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)