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: "automated hate speech detection and the problem of offensive language"
  Abstract: A key challenge for automatic hate-speech detection on social media is the separation of hate speech from other instances of offensive language. Lexical detection methods tend to have low precision because they classify all messages containing particular terms as hate speech and previous work using supervised learning has failed to distinguish between the two categories. We used a crowd-sourced hate speech lexicon to collect tweets containing hate speech keywords. We use crowd-sourcing to label a sample of these tweets into three categories: those containing hate speech, only offensive language, and those with neither. We train a multi-class classifier to distinguish between these different categories. Close analysis of the predictions and the errors shows when we can reliably separate hate speech from other offensive language and when this differentiation is more difficult. We find that racist and homophobic tweets are more likely to be classified as hate speech but that sexist tweets are generally classified as offensive. Tweets without explicit hate keywords are also more difficult to classify.

Connected through hyperedge HE-1 (induced by source paper "ten social dimensions of conversations and relationships"):
  with members:
    - "firsthand opiates abuse on social media monitoring geospatial patterns of interest through a digital cohort"
    - "the language of dialogue is complex"
    - "coloring in the links capturing social ties as they are perceived"
    - "the anatomy of reddit an overview of academic research"
    - "community interaction and conflict on the web"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "caveat emptor computational social science large scale missing data in a widely published reddit corpus"
    - "lstm a search space odyssey"

Connected through hyperedge HE-2 (induced by source paper "theory in theory out the uses of social theory in machine learning for social science"):
  with members:
    - "50 years of test un fairness lessons for machine learning"
    - "towards a critical race methodology in algorithmic fairness"
    - "racial categories in machine learning"
    - "garbage in garbage out do machine learning application papers in social computing report where human labeled training data comes from"
    - "privacy ethics and data access a case study of the fragile families challenge"
    - "an intersectional definition of fairness"
    - "attention is all you need"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"

Connected through hyperedge HE-3 (induced by source paper "offensive language identification in greek"):
  with members:
    - "offensive language and hate speech detection for danish"
    - "predicting the type and target of offensive posts in social media"
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"
    - "challenges in discriminating profanity from hate speech"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "understanding abuse a typology of abusive language detection subtasks"
    - "detecting hate speech in social media"
    - "a dictionary based approach to racism detection in dutch social media"

Connected through hyperedge HE-4 (induced by source paper "developing a multilingual annotated corpus of misogyny and aggression"):
  with members:
    - "when a tweet is actually sexist a more comprehensive classification of different online harassment categories and the challenges in nlp"
    - "predicting the type and target of offensive posts in social media"
    - "challenges in discriminating profanity from hate speech"
    - "aggression annotated corpus of hindi english code mixed data"
    - "understanding abuse a typology of abusive language detection subtasks"
    - "detecting hate speech in social media"
    - "characterizing linguistic attributes for automatic classification of intent based racist radicalized posts on tumblr micro blogging website"
    - "modeling trolling in social media conversations"

Connected through hyperedge HE-5 (induced by source paper "comparison between traditional machine learning models and neural network models for vietnamese hate speech detection"):
  with members:
    - "emotion recognition for vietnamese social media text"
    - "job prediction from deep neural network models to applications"
    - "vais hate speech detection system a deep learning based approach for system combination"
    - "hate speech detection on vietnamese social media text using the bidirectional lstm model"
    - "convolutional neural networks for toxic comment classification"
    - "detecting hate speech and offensive language on twitter using machine learning an n gram and tfidf based approach"
    - "hate speech detection a solved problem the challenging case of long tail on twitter"
    - "learning word vectors for 157 languages"

Connected through hyperedge HE-6 (induced by source paper "from pick up artists to incels a data driven sketch of the manosphere"):
  with members:
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "the effect of extremist violence on hateful speech online"
    - "analyzing right wing youtube channels hate violence and discrimination"
    - "on the origins of memes by means of fringe web communities"
    - "characterizing and detecting hateful users on twitter"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"

Connected through hyperedge HE-7 (induced by source paper "moralstrength exploiting a moral lexicon and embedding similarity for moral foundations prediction"):
  with members:
    - "human values and attitudes towards vaccination in social media"
    - "predicting demographics moral foundations and human values from digital behaviours"
    - "effect of values and technology use on exercise implications for personalized behavior change interventions"
    - "depechemood a bilingual emotion lexicon built through simple yet powerful techniques"
    - "quantifying moral foundations from various topics on twitter conversations"
    - "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews"

Connected through hyperedge HE-8 (induced by source paper "information credibility in the social web contexts approaches and open issues"):
  with members:
    - "on the inevitability of online echo chambers"
    - "combating fake news a survey on identification and mitigation techniques"
    - "automated fact checking task formulations methods and future directions"
    - "the spread of low credibility content by social bots"
    - "fake news detection on social media a data mining perspective"
    - "echo chambers in the age of misinformation"

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)