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 "the chameleon attack manipulating content display in online social media"):
  Abstract: Online social networks (OSNs) are ubiquitous attracting millions of users all over the world. Being a popular communication media OSNs are exploited in a variety of cyber attacks. In this article, we discuss the Chameleon attack technique, a new type of OSN-based trickery where malicious posts and profiles change the way they are displayed to OSN users to conceal themselves before the attack or avoid detection. Using this technique, adversaries can, for example, avoid censorship by concealing true content when it is about to be inspected; acquire social capital to promote new content while piggybacking a trending one; cause embarrassment and serious reputation damage by tricking a victim to like, retweet, or comment a message that he wouldn't normally do without any indication for the trickery within the OSN. An experiment performed with closed Facebook groups of sports fans shows that (1) Chameleon pages can pass by the moderation filters by changing the way their posts are displayed and (2) moderators do not distinguish between regular and Chameleon pages. We list the OSN weaknesses that facilitate the Chameleon attack and propose a set of mitigation guidelines.

Includes members:
  Member "stop clickbait detecting and preventing clickbaits in online news media":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards understanding the information ecosystem through the lens of multiple web communities")
  Member "manipulation and abuse on social media by emilio ferrara with ching man au yeung as coordinator":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning")

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)