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: "paying for likes understanding facebook like fraud using honeypots"
  Abstract: Facebook pages offer an easy way to reach out to a very large audience as they can easily be promoted using Facebook's advertising platform. Recently, the number of likes of a Facebook page has become a measure of its popularity and profitability, and an underground market of services boosting page likes, aka like farms, has emerged. Some reports have suggested that like farms use a network of profiles that also like other pages to elude fraud protection algorithms, however, to the best of our knowledge, there has been no systematic analysis of Facebook pages' promotion methods. #R##N#This paper presents a comparative measurement study of page likes garnered via Facebook ads and by a few like farms. We deploy a set of honeypot pages, promote them using both methods, and analyze garnered likes based on likers' demographic, temporal, and social characteristics. We highlight a few interesting findings, including that some farms seem to be operated by bots and do not really try to hide the nature of their operations, while others follow a stealthier approach, mimicking regular users' behavior.

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 "uncovering download fraud activities in mobile app markets"):
  with members:
    - "beyond google play a large scale comparative study of chinese android app markets"
    - "smoke screener or straight shooter detecting elite sybil attacks in user review social networks"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "fairplay fraud and malware detection in google play"
    - "xgboost a scalable tree boosting system"
    - "the dark side of micro task marketplaces characterizing fiverr and automatically detecting crowdturfing"
    - "serf and turf crowdturfing for fun and profit"

Connected through hyperedge HE-3 (induced by source paper "scalable similarity joins of tokenized strings"):
  with members:
    - "v smart join a scalable mapreduce framework for all pair similarity joins of multisets and vectors"
    - "pass join a partition based method for similarity joins"
    - "uncovering social network sybils in the wild"
    - "benchmarking declarative approximate selection predicates"

Connected through hyperedge HE-4 (induced by source paper "a decade of mal activity reporting a retrospective analysis of internet malicious activity blacklists"):
  with members:
    - "the chain of implicit trust an analysis of the web third party resources loading"
    - "serf and turf crowdturfing for fun and profit"

Connected through hyperedge HE-5 (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"
    - "klout score measuring influence across multiple social networks"
    - "people are strange when you re a stranger impact and influence of bots on social networks"
    - "what they do in shadows twitter underground follower market"
    - "serf and turf crowdturfing for fun and profit"

Connected through hyperedge HE-6 (induced by source paper "lobo evaluation of generalization deficiencies in twitter bot classifiers"):
  with members:
    - "poised spotting twitter spam off the beaten paths"
    - "discovery of the twitter bursty botnet"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "botornot a system to evaluate social bots"
    - "the darpa twitter bot challenge"

Connected through hyperedge HE-7 (induced by source paper "big data would not lie prediction of the 2016 taiwan election via online heterogeneous information"):
  with members:
    - "wikipedia traffic data and electoral prediction towards theoretically informed models"
    - "140 characters to victory using twitter to predict the uk 2015 general election"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-8 (induced by source paper "popularity evolution of professional users on facebook"):
  with members:
    - "predictability of popularity gaps between prediction and understanding"
    - "improving the effectiveness of content popularity prediction methods using time series trends"
    - "can cascades be predicted"
    - "using a model of social dynamics to predict popularity of news"
    - "predicting the popularity of online content"

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)