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: "klout score measuring influence across multiple social networks"
  Abstract: In this work, we present the Klout Score, an influence scoring system that assigns scores to 750 million users across 9 different social networks on a daily basis. We propose a hierarchical framework for generating an influence score for each user, by incorporating information for the user from multiple networks and communities. Over 3600 features that capture signals of influential interactions are aggregated across multiple dimensions for each user. The features are scalably generated by processing over 45 billion interactions from social networks every day, as well as by incorporating factors that indicate real world influence. Supervised models trained from labeled data determine the weights for features, and the final Klout Score is obtained by hierarchically combining communities and networks. We validate the correctness of the score by showing that users with higher scores are able to spread information more effectively in a network. Finally, we use several comparisons to other ranking systems to show that highly influential and recognizable users across different domains have high Klout scores.

Connected through hyperedge HE-1 (induced by source paper "midmod osn a microscopic level information diffusion model for online social networks"):
  with members:
    - "predicting online extremism content adopters and interaction reciprocity"
    - "when to post on social networks"
    - "predicting the temporal dynamics of information diffusion in social networks"
    - "diffusive logistic model towards predicting information diffusion in online social networks"

Connected through hyperedge HE-2 (induced by source paper "social influence and radicalization a social data analytics study"):
  with members:
    - "adaptive influence maximization in dynamic social networks"
    - "data curation apis"
    - "least cost influence maximization across multiple social networks"
    - "failedrevolutions using twitter to study the antecedents of isis support"
    - "online social media in the syria conflict encompassing the extremes and the in betweens"
    - "big data and cross document coreference resolution current state and future opportunities"
    - "maximizing social influence in nearly optimal time"
    - "on budgeted influence maximization in social networks"

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"
    - "who will retweet this automatically identifying and engaging strangers on twitter to spread information"
    - "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 "toward maximizing the visibility of content in social media brand pages a temporal analysis"):
  with members:
    - "online popularity under promotion viral potential forecasting and the economics of time"
    - "multistage campaigning in social networks"
    - "when to post on social networks"
    - "dynamical classes of collective attention in twitter"
    - "trends in social media persistence and decay"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"
    - "predicting the popularity of online content"

Connected through hyperedge HE-5 (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"
    - "measuring emotional contagion in social media"
    - "tweetcred real time credibility assessment of content on twitter"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-6 (induced by source paper "the follower count fallacy detecting twitter users with manipulated follower count"):
  with members:
    - "an army of me sockpuppets in online discussion communities"
    - "fairjudge trustworthy user prediction in rating platforms"
    - "detection of opinion spam based on anomalous rating deviation"
    - "fame for sale"
    - "people are strange when you re a stranger impact and influence of bots on social networks"
    - "paying for likes understanding facebook like fraud using honeypots"
    - "what they do in shadows twitter underground follower market"
    - "serf and turf crowdturfing for fun and profit"

Connected through hyperedge HE-7 (induced by source paper "exploring media bias and toxicity in south asian political discourse"):
  with members:
    - "measuring understanding and classifying news media sympathy on twitter after crisis events"
    - "140 characters to victory using twitter to predict the uk 2015 general election"

Connected through hyperedge HE-8 (induced by source paper "global entity ranking across multiple languages"):
  with members:
    - "high throughput and language agnostic entity disambiguation and linking on user generated data"
    - "learning from the news predicting entity popularity on twitter"
    - "entity ranking in wikipedia"

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)