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: "stfu noob predicting crowdsourced decisions on toxic behavior in online games"
  Abstract: One problem facing players of competitive games is negative, or toxic, behavior. League of Legends, the largest eSport game, uses a crowdsourcing platform called the Tribunal to judge whether a reported toxic player should be punished or not. The Tribunal is a two stage system requiring reports from those players that directly observe toxic behavior, and human experts that review aggregated reports. While this system has successfully dealt with the vague nature of toxic behavior by majority rules based on many votes, it naturally requires tremendous cost, time, and human efforts. #R##N#In this paper, we propose a supervised learning approach for predicting crowdsourced decisions on toxic behavior with large-scale labeled data collections; over 10 million user reports involved in 1.46 million toxic players and corresponding crowdsourced decisions. Our result shows good performance in detecting overwhelmingly majority cases and predicting crowdsourced decisions on them. We demonstrate good portability of our classifier across regions. Finally, we estimate the practical implications of our approach, potential cost savings and victim protection.

Connected through hyperedge HE-1 (induced by source paper "intergroup contact in the wild characterizing language differences between intergroup and single group members in nba related discussion forums"):
  with members:
    - "this is why we play characterizing online fan communities of the nba teams"
    - "community interaction and conflict on the web"
    - "extracting inter community conflicts in reddit"
    - "me my echo chamber and i introspection on social media polarization"
    - "automated hate speech detection and the problem of offensive language"
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "antisocial behavior in online discussion communities"

Connected through hyperedge HE-2 (induced by source paper "community interaction and conflict on the web"):
  with members:
    - "false information on web and social media a survey"
    - "community identity and user engagement in a multi community landscape"
    - "an army of me sockpuppets in online discussion communities"
    - "contagion dynamics of extremist propaganda in social networks"
    - "representation learning on graphs methods and applications"
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "everything i disagree with is fakenews correlating political polarization and spread of misinformation"
    - "loyalty in online communities"

Connected through hyperedge HE-3 (induced by source paper "a unified deep learning architecture for abuse detection"):
  with members:
    - "challenges in discriminating profanity from hate speech"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "effective hate speech detection in twitter data using recurrent neural networks"
    - "understanding abuse a typology of abusive language detection subtasks"
    - "mean birds detecting aggression and bullying on twitter"
    - "automated hate speech detection and the problem of offensive language"
    - "deep learning for hate speech detection in tweets"
    - "one step and two step classification for abusive language detection on twitter"

Connected through hyperedge HE-4 (induced by source paper "machine learning and semantic analysis of in game chat for cyberbullying"):
  with members:
    - "cyberbullying identification using participant vocabulary consistency"
    - "exploring cyberbullying and other toxic behavior in team competition online games"
    - "linguistic analysis of toxic behavior in an online video game"

Connected through hyperedge HE-5 (induced by source paper "i would not plant apple trees if the world will be wiped analyzing hundreds of millions of behavioral records of players during an mmorpg beta test"):
  with members:
    - "exploring cyberbullying and other toxic behavior in team competition online games"
    - "linguistic analysis of toxic behavior in an online video game"
    - "comparing and combining sentiment analysis methods"
    - "modeling epidemic spread in synthetic populations virtual plagues in massively multiplayer online games"

Connected through hyperedge HE-6 (induced by source paper "achievement and friends key factors of player retention vary across player levels in online multiplayer games"):
  with members:
    - "exploring cyberbullying and other toxic behavior in team competition online games"
    - "persistent sharing of fitness app status on twitter"
    - "the proficiency congruency dilemma virtual team design and performance in multiplayer online games"
    - "linguistic analysis of toxic behavior in an online video game"

Connected through hyperedge HE-7 (induced by source paper "a tutor agent for moba games"):
  with members:
    - "exploring cyberbullying and other toxic behavior in team competition online games"

Connected through hyperedge HE-8 (induced by source paper "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"):
  with members:
    - "automatic verbal aggression detection for russian and american imageboards"
    - "detecting state of aggression in sentences using cnn"
    - "antisocial behavior in online discussion communities"
    - "experimental analysis of popular smartphone apps offering anonymity ephemerality and end to end encryption"
    - "clustering memes in social media"

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)