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: "quantifying the effect of sentiment on information diffusion in social media"
  Abstract: Social media has become the main vehicle of information production and consumption online. Millions of users every day log on their Facebook or Twitter accounts to get updates and news, read about their topics of interest, and become exposed to new opportunities and interactions. Although recent studies suggest that the contents users produce will affect the emotions of their readers, we still lack a rigorous understanding of the role and effects of contents sentiment on the dynamics of information diffusion. #R##N#This work aims at quantifying the effect of sentiment on information diffusion, to understand: #R##N#(i) whether positive conversations spread faster and/or broader than negative ones (or vice-versa); #R##N#(ii) what kind of emotions are more typical of popular conversations on social media; and, #R##N#(iii) what type of sentiment is expressed in conversations characterized by different temporal dynamics. #R##N#Our findings show that, at the level of contents, negative messages spread faster than positive ones, but positive ones reach larger audiences, suggesting that people are more inclined to share and favorite positive contents, the so-called positive bias. As for the entire conversations, we highlight how different temporal dynamics exhibit different sentiment patterns: for example, positive sentiment builds up for highly-anticipated events, while unexpected events are mainly characterized by negative sentiment. Our contribution represents a step forward to understand how the emotions expressed in short texts correlate with their spreading in online social ecosystems, and may help to craft effective policies and strategies for content generation and diffusion.

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 "exploiting emotions for fake news detection on social media"):
  with members:
    - "false news detection on social media"
    - "learning multimodal word representation via dynamic fusion methods"
    - "beyond news contents the role of social context for fake news detection"
    - "fake news detection on social media a data mining perspective"
    - "csi a hybrid deep model for fake news detection"
    - "some like it hoax automated fake news detection in social networks"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "iad interaction aware diffusion framework in social networks"):
  with members:
    - "anger is more influential than joy sentiment correlation in weibo"
    - "modeling adoption and usage of competing products"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "diffusive logistic model towards predicting information diffusion in online social networks"

Connected through hyperedge HE-5 (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-6 (induced by source paper "propagation from deceptive news sources who shares how much how evenly and how quickly"):
  with members:
    - "the spread of low credibility content by social bots"
    - "contagion dynamics of extremist propaganda in social networks"
    - "the spread of fake news by social bots"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "rating effects on social news posts and comments"
    - "the rise of social bots"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"

Connected through hyperedge HE-7 (induced by source paper "early detection of promoted campaigns on social media"):
  with members:
    - "online human bot interactions detection estimation and characterization"
    - "predicting online extremism content adopters and interaction reciprocity"
    - "botornot a system to evaluate social bots"
    - "the darpa twitter bot challenge"
    - "do cascades recur"
    - "the rise of social bots"
    - "science vs conspiracy collective narratives in the age of misinformation"
    - "computational fact checking from knowledge 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)