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: "who will retweet this automatically identifying and engaging strangers on twitter to spread information"
  Abstract: There has been much effort on studying how social media sites, such as Twitter, help propagate information in different situations, including spreading alerts and SOS messages in an emergency. However, existing work has not addressed how to actively identify and engage the right strangers at the right time on social media to help effectively propagate intended information within a desired time frame. To address this problem, we have developed two models: (i) a feature-based model that leverages peoples' exhibited social behavior, including the content of their tweets and social interactions, to characterize their willingness and readiness to propagate information on Twitter via the act of retweeting; and (ii) a wait-time model based on a user's previous retweeting wait times to predict her next retweeting time when asked. Based on these two models, we build a recommender system that predicts the likelihood of a stranger to retweet information when asked, within a specific time window, and recommends the top-N qualified strangers to engage with. Our experiments, including live studies in the real world, demonstrate the effectiveness of our work.

Connected through hyperedge HE-1 (induced by source paper "the art of social bots a review and a refined taxonomy"):
  with members:
    - "cashtag piggybacking uncovering spam and bot activity in stock microblogs on twitter"
    - "sybilfuse combining local attributes with global structure to perform robust sybil detection"
    - "lobo evaluation of generalization deficiencies in twitter bot classifiers"
    - "false information on web and social media a survey"
    - "measuring social spam and the effect of bots on information diffusion in social media"
    - "detecting influence campaigns in social networks using the ising model"
    - "botnet campaign detection on twitter"
    - "an unsupervised approach to detect spam campaigns that use botnets on twitter"

Connected through hyperedge HE-2 (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-3 (induced by source paper "towards successful social media advertising predicting the influence of commercial tweets"):
  with members:
    - "tweet2vec character based distributed representations for social media"
    - "tweet2vec learning tweet embeddings using character level cnn lstm encoder decoder"
    - "klout score measuring influence across multiple social networks"
    - "distributed representations of sentences and documents"
    - "can cascades be predicted"
    - "efficient estimation of word representations in vector space"
    - "a new anew evaluation of a word list for sentiment analysis in microblogs"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-4 (induced by source paper "the rise of guardians fact checking url recommendation to combat fake news"):
  with members:
    - "mrattractor detecting communities from large scale graphs"
    - "leveraging the crowd to detect and reduce the spread of fake news and misinformation"
    - "csi a hybrid deep model for fake news detection"
    - "online human bot interactions detection estimation and characterization"
    - "hoaxy a platform for tracking online misinformation"
    - "analysing how people orient to and spread rumours in social media by looking at conversational threads"
    - "distributed representations of sentences and documents"
    - "collaborative deep learning for recommender systems"

Connected through hyperedge HE-5 (induced by source paper "real time detection of content polluters in partially observable twitter networks"):
  with members:
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "online human bot interactions detection estimation and characterization"
    - "privacy and security in online social networks a survey"
    - "botornot a system to evaluate social bots"
    - "the darpa twitter bot challenge"
    - "the rise of social bots"
    - "making the most of tweet inherent features for social spam detection on twitter"

Connected through hyperedge HE-6 (induced by source paper "peer to peer hate hate speech instigators and their targets"):
  with members:
    - "mean birds detecting aggression and bullying on twitter"
    - "automated hate speech detection and the problem of offensive language"
    - "a web of hate tackling hateful speech in online social spaces"
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "hate is not binary studying abusive behavior of gamergate on twitter"
    - "analyzing the targets of hate in online social media"
    - "antisocial behavior in online discussion communities"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"

Connected through hyperedge HE-7 (induced by source paper "confiding in and listening to virtual agents the effect of personality"):
  with members:
    - "recommending targeted strangers from whom to solicit information on social media"
    - "interpreting social cues to generate credible affective reactions of virtual job interviewers"

Connected through hyperedge HE-8 (induced by source paper "identifying on time reward delivery projects with estimating delivery duration on kickstarter"):
  with members:
    - "how to succeed in crowdfunding a long term study in kickstarter"
    - "donor retention in online crowdfunding communities a case study of donorschoose org"
    - "recommending investors for crowdfunding projects"

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)