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 "measuring emotional contagion in social media"):
  Abstract: Social media are used as main discussion channels by millions of individuals every day. The content individuals produce in daily social-media-based micro-communications, and the emotions therein expressed, may impact the emotional states of others. A recent experiment performed on Facebook hypothesized that emotions spread online, even in absence of non-verbal cues typical of in-person interactions, and that individuals are more likely to adopt positive or negative emotions if these are over-expressed in their social network. Experiments of this type, however, raise ethical concerns, as they require massive-scale content manipulation with unknown consequences for the individuals therein involved. Here, we study the dynamics of emotional contagion using a random sample of Twitter users, whose activity (and the stimuli they were exposed to) was observed during a week of September 2014. Rather than manipulating content, we devise a null model that discounts some confounding factors (including the effect of emotional contagion). We measure the emotional valence of content the users are exposed to before posting their own tweets. We determine that on average a negative post follows an over-exposure to 4.34% more negative content than baseline, while positive posts occur after an average over-exposure to 4.50% more positive contents. We highlight the presence of a linear relationship between the average emotional valence of the stimuli users are exposed to, and that of the responses they produce. We also identify two different classes of individuals: highly and scarcely susceptible to emotional contagion. Highly susceptible users are significantly less inclined to adopt negative emotions than the scarcely susceptible ones, but equally likely to adopt positive emotions. In general, the likelihood of adopting positive emotions is much greater than that of negative emotions.

Includes members:
  Member "on predictability of rare events leveraging social media a machine learning perspective":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "quantifying the effect of sentiment on information diffusion in social media")
  Member "the simple rules of social contagion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural diffusion model for microscopic cascade prediction")
  Member "quantifying the effect of sentiment on information diffusion in social media":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bots elections and social media a brief overview")
  Member "on facebook most ties are weak":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the history of digital spam")
  Member "evolution of online user behavior during a social upheaval":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mining social media data for biomedical signals and health related behavior")
  Member "can cascades be predicted":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep collaborative embedding for information cascade prediction")
  Member "romantic partnerships and the dispersion of social ties a network analysis of relationship status on facebook":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "locec local community based edge classification in large online social networks")
  Member "the digital evolution of occupy wall street":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "bots elections and social media a brief overview")

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)