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 "sentiment dynamics in social media news channels"):
  Abstract: Social media is currently one of the most important means of news communication. Since people are consuming a large fraction of their daily news through social media, most of the traditional news channels are using social media to catch the attention of users. Each news channel has its own strategies to attract more users. In this paper, we analyze how the news channels use sentiment to garner users' attention in social media. We compare the sentiment of social media news posts of television, radio and print media, to show the differences in the ways these channels cover the news. We also analyze users' reactions and opinion sentiment on news posts with different sentiments. We perform our experiments on a dataset extracted from Facebook Pages of five popular news channels. Our dataset contains 0.15 million news posts and 1.13 billion users reactions. The results of our experiments show that the sentiment of user opinion has a strong correlation with the sentiment of the news post and the type of information source. Our study also illustrates the differences among the social media news channels of different types of news sources.

Includes members:
  Member "automated hate speech detection and the problem of offensive language":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ten social dimensions of conversations and relationships")
  Member "anyone can become a troll causes of trolling behavior in online discussions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ten social dimensions of conversations and relationships")
  Member "breaking the news first impressions matter on online news":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to determine the quality of news headlines")
  Member "characterizing the life cycle of online news stories using social media reactions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scilens evaluating the quality of scientific news articles using social media and scientific literature indicators")
  Member "how community feedback shapes user behavior":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "trajectories of blocked community members redemption recidivism and departure")
  Member "the pulse of news in social media forecasting popularity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "modeling the popularity of twitter hashtags with master equations")
  Member "good friends bad news affect and virality in twitter":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the complexity of social media response statistical evidence for one dimensional engagement signal in twitter")
  Member "using a model of social dynamics to predict popularity of news":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning")

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)