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: "mind your pov convergence of articles and editors towards wikipedia s neutrality norm"
  Abstract: Wikipedia has a strong norm of writing in a 'neutral point of view' (NPOV). Articles that violate this norm are tagged, and editors are encouraged to make corrections. But the impact of this tagging system has not been quantitatively measured. Does NPOV tagging help articles to converge to the desired style? Do NPOV corrections encourage editors to adopt this style? We study these questions using a corpus of NPOV-tagged articles and a set of lexicons associated with biased language. An interrupted time series analysis shows that after an article is tagged for NPOV, there is a significant decrease in biased language in the article, as measured by several lexicons. However, for individual editors, NPOV corrections and talk page discussions yield no significant change in the usage of words in most of these lexicons, including Wikipedia's own list of 'words to watch.' This suggests that NPOV tagging and discussion does improve content, but has less success enculturating editors to the site's linguistic norms.

Connected through hyperedge HE-1 (induced by source paper "trajectories of blocked community members redemption recidivism and departure"):
  with members:
    - "forecasting the presence and intensity of hostility on instagram using linguistic and social features"
    - "the effect of extremist violence on hateful speech online"
    - "community interaction and conflict on the web"
    - "wikiconv a corpus of the complete conversational history of a large online collaborative community"
    - "the view from the other side the border between controversial speech and harassment on kotaku in action"
    - "conversations gone awry detecting early signs of conversational failure"
    - "characterizing and detecting hateful users on twitter"
    - "automated hate speech detection and the problem of offensive language"

Connected through hyperedge HE-2 (induced by source paper "content removal as a moderation strategy compliance and other outcomes in the changemyview community"):
  with members:
    - "trajectories of blocked community members redemption recidivism and departure"
    - "trouble on the horizon forecasting the derailment of online conversations as they develop"
    - "forecasting the presence and intensity of hostility on instagram using linguistic and social features"
    - "reducing gender bias in abusive language detection"
    - "conversations gone awry detecting early signs of conversational failure"
    - "a warm welcome matters the link between social feedback and weight loss in r loseit"
    - "measuring offensive speech in online political discourse"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"

Connected through hyperedge HE-3 (induced by source paper "hateful people or hateful bots detection and characterization of bots spreading religious hatred in arabic social media"):
  with members:
    - "universal dependency parsing from scratch"
    - "deep learning for hate speech detection in tweets"
    - "online human bot interactions detection estimation and characterization"
    - "botornot a system to evaluate social bots"
    - "dissecting a social botnet growth content and influence in twitter"
    - "analyzing the targets of hate in online social media"
    - "the rise of social bots"
    - "content and network dynamics behind egyptian political polarization on twitter"

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.CL(Computation and Language)