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: "influence and passivity in social media"
  Abstract: The ever-increasing amount of information flowing through Social Media forces the members of these networks to compete for attention and influence by relying on other people to spread their message. A large study of information propagation within Twitter reveals that the majority of users act as passive information consumers and do not forward the content to the network. Therefore, in order for individuals to become influential they must not only obtain attention and thus be popular, but also overcome user passivity. We propose an algorithm that determines the influence and passivity of users based on their information forwarding activity. An evaluation performed with a 2.5 million user dataset shows that our influence measure is a good predictor of URL clicks, outperforming several other measures that do not explicitly take user passivity into account. We also explicitly demonstrate that high popularity does not necessarily imply high influence and vice-versa.

Connected through hyperedge HE-1 (induced by source paper "online reactions to the 2017 unite the right rally in charlottesville measuring polarization in twitter networks using media followership"):
  with members:
    - "social network analysis for social neuroscientists"
    - "its all in a name detecting and labeling bots by their name"
    - "centrality in modular networks"
    - "a quantitative approach to understanding online antisemitism"
    - "who let the trolls out towards understanding state sponsored trolls"
    - "on the origins of memes by means of fringe web communities"
    - "a long term analysis of polarization on twitter"
    - "botornot a system to evaluate social bots"

Connected through hyperedge HE-2 (induced by source paper "a user modeling pipeline for studying polarized political events in social media"):
  with members:
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "online human bot interactions detection estimation and characterization"
    - "botornot a system to evaluate social bots"
    - "distributed representations of words and phrases and their compositionality"
    - "a meta analysis of state of the art electoral prediction from twitter data"
    - "truthy mapping the spread of astroturf in microblog streams"

Connected through hyperedge HE-3 (induced by source paper "white man and highly followed gender and race inequalities in twitter"):
  with members:
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "demographics of news sharing in the u s twittersphere"
    - "linguistic diversities of demographic groups in twitter"
    - "who makes trends understanding demographic biases in crowdsourced recommendations"
    - "from migration corridors to clusters the value of google data for migration studies"
    - "women through the glass ceiling gender asymmetries in wikipedia"
    - "greysanatomy vs yankees demographics and hashtag use on twitter"
    - "inferring gender from names on the web a comparative evaluation of gender detection methods"

Connected through hyperedge HE-4 (induced by source paper "demographics of news sharing in the u s twittersphere"):
  with members:
    - "what gets media attention and how media attention evolves over time large scale empirical evidence from 196 countries"
    - "who makes trends understanding demographic biases in crowdsourced recommendations"
    - "greysanatomy vs yankees demographics and hashtag use on twitter"
    - "inferring gender from names on the web a comparative evaluation of gender detection methods"
    - "it s a man s wikipedia assessing gender inequality in an online encyclopedia"
    - "breaking the news first impressions matter on online news"
    - "when is it biased assessing the representativeness of twitter s streaming api"
    - "learning deep face representation"

Connected through hyperedge HE-5 (induced by source paper "tweet for behavior change using social media for the dissemination of public health messages"):
  with members:
    - "big questions for social media big data representativeness validity and other methodological pitfalls"
    - "diffusive logistic model towards predicting information diffusion in online social networks"

Connected through hyperedge HE-6 (induced by source paper "comparative opinion mining a review"):
  with members:
    - "introduction to the conll 2000 shared task chunking"

Connected through hyperedge HE-7 (induced by source paper "measuring user influence on twitter"):
  with members:
    - "a review of features for the discrimination of twitter users application to the prediction of offline influence"
    - "truetop a sybil resilient system for user influence measurement on twitter"
    - "klout score measuring influence across multiple social networks"
    - "detecting real world influence through twitter"
    - "influencetracker rating the impact of a twitter account"
    - "nepotistic relationships in twitter and their impact on rank prestige algorithms"
    - "de retibus socialibus et legibus momenti"

Connected through hyperedge HE-8 (induced by source paper "topicality and impact in social media diverse messages focused messengers"):
  with members:
    - "can cascades be predicted"
    - "bigbirds never die understanding social dynamics of emergent hashtag"
    - "the role of information diffusion in the evolution of social networks"
    - "clustering memes in social media"
    - "virality prediction and community structure in social networks"
    - "real time discovery of dense clusters in highly dynamic graphs identifying real world events in highly dynamic environments"
    - "on the interplay between social and topical structure"
    - "folks in folksonomies social link prediction from shared metadata"

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.CY(Computers and Society)