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: "trollspot detecting misbehavior in commenting platforms"
  Abstract: Commenting platforms, such as Disqus, have emerged as a major online communication platform with millions of users and posts. Their popularity has also attracted parasitic and malicious behav- iors, such as trolling and spamming. There has been relatively little research on modeling and safeguarding these platforms. As our key contribution, we develop a systematic approach to detect malicious users on commenting platforms focusing on having: (a) interpretable, and (b) fine-grained classification of malice. Our work has two key novelties: (a) we propose two classifications methods, with one following a two stage approach, which first maps observ- able features to behaviors and then maps these behaviors to user roles, and (b) we use a comprehensive set of 73 features that span four dimensions of information. We use 7 million comments during a 9 month period, and we show that our classification methods can distinguish between benign, and malicious roles (spammers, trollers, and fanatics) with a 0.904 AUC. Our work is a solid step to- wards ensuring that commenting platforms are a safe and pleasant medium for the exchange of ideas.

Connected through hyperedge HE-1 (induced by source paper "analyzing right wing youtube channels hate violence and discrimination"):
  with members:
    - "like sheep among wolves characterizing hateful users on twitter"
    - "measuring gamergate a tale of hate sexism and bullying"
    - "semantics derived automatically from language corpora contain human like biases"
    - "empath understanding topic signals in large scale text"
    - "shirtless and dangerous quantifying linguistic signals of gender bias in an online fiction writing community"
    - "participatory militias an analysis of an armed movement s online audience"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "mining actionable information from security forums the case of malicious ip addresses"):
  with members:
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "online actions with offline impact how online social networks influence online and offline user behavior"
    - "automatic labeling for entity extraction in cyber security"
    - "the anatomy of the facebook social graph"

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)