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: "hybrid approaches to detect comments violating macro norms on reddit"
  Abstract: In this dataset paper, we present a three-stage process to collect Reddit comments that are removed comments by moderators of several subreddits, for violating subreddit rules and guidelines. Other than the fact that these comments were flagged by moderators for violating community norms, we do not have any other information regarding the nature of the violations. Working with over 2.8M removed comments collected from 100 different communities on Reddit, we identify 8 macro norms (i.e., norms that are widely enforced on most parts of Reddit). We extract these macro norms by employing a hybrid approach (classification, topic modeling, and open-coding), on comments identified to be norm violations within at least 85 out of the 100 study subreddits. Finally, we label over 40K Reddit comments removed by moderators according to the specific type of macro norm being violated, and make this dataset publicly available. By breaking down a collection of removed comments into more granular types of macro norm violation, our dataset can be used to train more nuanced machine learning classifiers for online moderation.

Connected through hyperedge HE-1 (induced by source paper "a just and comprehensive strategy for using nlp to address online abuse"):
  with members:
    - "machine learning suites for online toxicity detection"
    - "forecasting the presence and intensity of hostility on instagram using linguistic and social features"
    - "a survey on natural language processing for fake news detection"
    - "style transfer through back translation"
    - "thou shalt not hate countering online hate speech"
    - "extractive adversarial networks high recall explanations for identifying personal attacks in social media posts"
    - "fighting offensive language on social media with unsupervised text style transfer"
    - "conversations gone awry detecting early signs of conversational failure"

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)