human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "combating fraud in online social networks characterizing and detecting facebook like farms"):
  Abstract: As businesses increasingly rely on social networking sites to engage with their customers, it is crucial to understand and counter reputation manipulation activities, including fraudulently boosting the number of Facebook page likes using so-called like farms. Thus, social network operators have started to deploy various fraud detection algorithms such as graph clustering methods, however, with limited efficacy. In fact, this paper presents a comprehensive analysis and evaluation of existing graph-based fraud detection algorithms for detecting like farm accounts. Our results show that more sophisticated and stealthy farms can successfully evade detection by spreading likes over longer timespans and by liking many popular pages to mimic normal users. #R##N#Next, we analyze a wide range of features extracted from users' timeline posts, which we group into two main classes: lexical and interaction-based. We find that like farm accounts tend to more often re-share content, use fewer words and poorer vocabulary, target fewer topics, and generate more (often duplicate) comments and likes compared to normal users. Using these timeline-based features, we experiment with machine learning algorithms to detect like farms accounts, obtaining appreciably high accuracy (as high as 99% precision and 97% recall).

Includes members:
  Member "paying for likes understanding facebook like fraud using honeypots":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the art of social bots a review and a refined taxonomy")
  Member "api design for machine learning software experiences from the scikit learn project":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to segment 3d point clouds in 2d image space")

Question: Please tell me which class the source paper 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)