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: "good friends bad news affect and virality in twitter"
  Abstract: The link between affect, defined as the capacity for sentimental arousal on the part of a message, and virality, defined as the probability that it be sent along, is of significant theoretical and practical importance, e.g. for viral marketing. A quantitative study of emailing of articles from the NY Times finds a strong link between positive affect and virality, and, based on psychological theories it is concluded that this relation is universally valid. The conclusion appears to be in contrast with classic theory of diffusion in news media emphasizing negative affect as promoting propagation. In this paper we explore the apparent paradox in a quantitative analysis of information diffusion on Twitter. Twitter is interesting in this context as it has been shown to present both the characteristics social and news media. The basic measure of virality in Twitter is the probability of retweet. Twitter is different from email in that retweeting does not depend on pre-existing social relations, but often occur among strangers, thus in this respect Twitter may be more similar to traditional news media. We therefore hypothesize that negative news content is more likely to be retweeted, while for non-news tweets positive sentiments support virality. To test the hypothesis we analyze three corpora: A complete sample of tweets about the COP15 climate summit, a random sample of tweets, and a general text corpus including news. The latter allows us to train a classifier that can distinguish tweets that carry news and non-news information. We present evidence that negative sentiment enhances virality in the news segment, but not in the non-news segment. We conclude that the relation between affect and virality is more complex than expected based on the findings of Berger and Milkman (2010), in short 'if you want to be cited: Sweet talk your friends or serve bad news to the public'.

Connected through hyperedge HE-1 (induced by source paper "sentiment dynamics in social media news channels"):
  with members:
    - "automated hate speech detection and the problem of offensive language"
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "breaking the news first impressions matter on online news"
    - "characterizing the life cycle of online news stories using social media reactions"
    - "how community feedback shapes user behavior"
    - "the pulse of news in social media forecasting popularity"
    - "using a model of social dynamics to predict popularity of news"
    - "predicting the popularity of online content"

Connected through hyperedge HE-2 (induced by source paper "the complexity of social media response statistical evidence for one dimensional engagement signal in twitter"):
  with members:
    - "scalable privacy compliant virality prediction on twitter"
    - "limited individual attention and online virality of low quality information"
    - "quote rts on twitter usage of the new feature for political discourse"

Connected through hyperedge HE-3 (induced by source paper "measuring understanding and classifying news media sympathy on twitter after crisis events"):
  with members:
    - "are you charlie or ahmed cultural pluralism in charlie hebdo response on twitter"
    - "a primer on neural network models for natural language processing"
    - "controversy and sentiment in online news"
    - "convolutional neural networks for sentence classification"
    - "content and network dynamics behind egyptian political polarization on twitter"
    - "understanding news geography and major determinants of global news coverage of disasters"
    - "efficient estimation of word representations in vector space"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-4 (induced by source paper "scalable privacy compliant virality prediction on twitter"):
  with members:
    - "what do we need to build explainable ai systems for the medical domain"
    - "mllib machine learning in apache spark"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "can cascades be predicted"

Connected through hyperedge HE-5 (induced by source paper "sarcasmdetection is soooo general towards a domain independent approach for detecting sarcasm"):
  with members:
    - "modelling context with user embeddings for sarcasm detection in social media"
    - "how transferable are features in deep neural networks"
    - "frustratingly easy domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "on the role of conductance geography and topology in predicting hashtag virality"):
  with members:
    - "can cascades be predicted"
    - "a bayesian approach for predicting the popularity of tweets"
    - "predicting successful memes using network and community structure"
    - "exploring image virality in google plus"
    - "virality prediction and community structure in social networks"
    - "exploring text virality in social networks"
    - "topic diffusion and emergence of virality in social networks"
    - "information diffusion and external influence in networks"

Connected through hyperedge HE-7 (induced by source paper "deep feelings a massive cross lingual study on the relation between emotions and virality"):
  with members:
    - "depechemood a lexicon for emotion analysis from crowd annotated news"
    - "anger is more influential than joy sentiment correlation in weibo"
    - "the role of emotions in propagating brands in social networks"
    - "how to ask for a favor a case study on the success of altruistic requests"
    - "the effect of wording on message propagation topic and author controlled natural experiments on twitter"
    - "exploring image virality in google plus"
    - "the untold story of the clones content agnostic factors that impact youtube video popularity"
    - "exploring text virality in social networks"

Connected through hyperedge HE-8 (induced by source paper "all who wander on the prevalence and characteristics of multi community engagement"):
  with members:
    - "evolution of reddit from the front page of the internet to a self referential community"
    - "how community feedback shapes user behavior"
    - "the untold story of the clones content agnostic factors that impact youtube video popularity"
    - "exploring text virality in social networks"
    - "do linguistic style and readability of scientific abstracts affect their virality"
    - "you had me at hello how phrasing affects memorability"
    - "on the interplay between social and topical structure"
    - "mark my words linguistic style accommodation in social media"

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)