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: "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.

Connected through hyperedge HE-1 (induced by source paper "bots elections and social media a brief overview"):
  with members:
    - "the history of digital spam"
    - "red bots do it better comparative analysis of social bot partisan behavior"
    - "arming the public with artificial intelligence to counter social bots"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "deep neural networks for bot detection"
    - "measuring bot and human behavioral dynamics"
    - "who falls for online political manipulation"
    - "the spread of low credibility content by social bots"

Connected through hyperedge HE-2 (induced by source paper "perils and challenges of social media and election manipulation analysis the 2018 us midterms"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "senator we sell ads analysis of the 2016 russian facebook ads campaign"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "deep neural networks for bot detection"
    - "who falls for online political manipulation"
    - "the spread of low credibility content by social bots"
    - "fake news detection on social media a data mining perspective"
    - "early detection of promoted campaigns on social media"

Connected through hyperedge HE-3 (induced by source paper "identifying long term periodic cycles and memories of collective emotion in online social media"):
  with members:
    - "self organization on social media endo exo bursts and baseline fluctuations"
    - "distributed representations of sentences and documents"
    - "analysing mood patterns in the united kingdom through twitter content"
    - "distributed representations of words and phrases and their compositionality"
    - "panas t a pychometric scale for measuring sentiments on twitter"
    - "detecting events and patterns in large scale user generated textual streams with statistical learning methods"
    - "twitter mood predicts the stock market"
    - "measuring the happiness of large scale written expression songs blogs and presidents"

Connected through hyperedge HE-4 (induced by source paper "what sets verified users apart insights analysis and prediction of verified users on twitter"):
  with members:
    - "elites tweet characterizing the twitter verified user network"
    - "election bias comparing polls and twitter in the 2016 u s election"
    - "an in depth characterisation of bots and humans on twitter"
    - "online human bot interactions detection estimation and characterization"
    - "klout score measuring influence across multiple social networks"
    - "tweetcred real time credibility assessment of content on twitter"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-5 (induced by source paper "tweet moodifier towards giving emotional awareness to twitter users"):
  with members:
    - "ex machina personal attacks seen at scale"
    - "concrete problems in ai safety"
    - "anger is more influential than joy sentiment correlation in weibo"

Connected through hyperedge HE-6 (induced by source paper "measuring social spam and the effect of bots on information diffusion in social media"):
  with members:
    - "contagion dynamics of extremist propaganda in social networks"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "early detection of promoted campaigns on social media"
    - "online human bot interactions detection estimation and characterization"
    - "botornot a system to evaluate social bots"
    - "social politics agenda setting and political communication on social media"
    - "dissecting a social botnet growth content and influence in twitter"
    - "the darpa twitter bot challenge"

Connected through hyperedge HE-7 (induced by source paper "an agent based model for emotion contagion and competition in online social media"):
  with members:
    - "limited individual attention and online virality of low quality information"
    - "anger is more influential than joy sentiment correlation in weibo"
    - "agent based model for rural urban migration a dynamic consideration"
    - "the role of social networks in information diffusion"
    - "negative emotions boost user activity at bbc forum"
    - "twitter reciprocal reply networks exhibit assortativity with respect to happiness"
    - "happiness is assortative in online social networks"
    - "weak ties subtle role of information diffusion in online social networks"

Connected through hyperedge HE-8 (induced by source paper "contagion dynamics of extremist propaganda in social networks"):
  with members:
    - "the rise of jihadist propaganda on social networks"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "online human bot interactions detection estimation and characterization"
    - "predicting online extremism content adopters and interaction reciprocity"
    - "the darpa twitter bot challenge"
    - "spider and the flies focused crawling on tumblr to detect hate promoting communities"
    - "network structure and resilience of mafia syndicates"
    - "the rise of social bots"

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)