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: "the many faces of link fraud"
  Abstract: Most past work on social network link fraud detection tries to separate genuine users from fraudsters, implicitly assuming that there is only one type of fraudulent behavior. But is this assumption true? And, in either case, what are the characteristics of such fraudulent behaviors? In this work, we set up honeypots ("dummy" social network accounts), and buy fake followers (after careful IRB approval). We report the signs of such behaviors including oddities in local network connectivity, account attributes, and similarities and differences across fraud providers. Most valuably, we discover and characterize several types of fraud behaviors. We discuss how to leverage our insights in practice by engineering strongly performing entropy-based features and demonstrating high classification accuracy. Our contributions are (a) instrumentation: we detail our experimental setup and carefully engineered data collection process to scrape Twitter data while respecting API rate-limits, (b) observations on fraud multimodality: we analyze our honeypot fraudster ecosystem and give surprising insights into the multifaceted behaviors of these fraudster types, and (c) features: we propose novel features that give strong (>0.95 precision/recall) discriminative power on ground-truth Twitter data.

Connected through hyperedge HE-1 (induced by source paper "multitask learning for blackmarket tweet detection"):
  with members:
    - "girnet interleaved multi task recurrent state sequence models"
    - "retweet us we will retweet you spotting collusive retweeters involved in blackmarket services"
    - "a survey on multi task learning"
    - "tweet2vec character based distributed representations for social media"
    - "botornot a system to evaluate social bots"
    - "cross stitch networks for multi task learning"

Connected through hyperedge HE-2 (induced by source paper "silcendice mining suspicious multi attribute entity groups with multi view graphs"):
  with members:
    - "false information on web and social media a survey"
    - "edgecentric anomaly detection in edge attributed networks"

Connected through hyperedge HE-3 (induced by source paper "false information on web and social media a survey"):
  with members:
    - "detection and resolution of rumours in social media a survey"
    - "the spread of low credibility content by social bots"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "leveraging the crowd to detect and reduce the spread of fake news and misinformation"
    - "an army of me sockpuppets in online discussion communities"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"
    - "automatic detection of fake news"
    - "beyond news contents the role of social context for fake news detection"

Connected through hyperedge HE-4 (induced by source paper "retweet us we will retweet you spotting collusive retweeters involved in blackmarket services"):
  with members:
    - "the follower count fallacy detecting twitter users with manipulated follower count"
    - "collective classification of spam campaigners on twitter a hierarchical meta path based approach"
    - "botornot a system to evaluate social bots"
    - "the rise of social bots"

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)