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: "understanding abuse a typology of abusive language detection subtasks"
  Abstract: As the body of research on abusive language detection and analysis grows, there is a need for critical consideration of the relationships between different subtasks that have been grouped under this label. Based on work on hate speech, cyberbullying, and online abuse we propose a typology that captures central similarities and differences between subtasks and we discuss its implications for data annotation and feature construction. We emphasize the practical actions that can be taken by researchers to best approach their abusive language detection subtask of interest.

Connected through hyperedge HE-1 (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"
    - "automated hate speech detection and the problem of offensive language"
    - "detecting hate speech in social media"
    - "a dictionary based approach to racism detection in dutch social media"

Connected through hyperedge HE-2 (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"
    - "automated hate speech detection and the problem of offensive language"
    - "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-3 (induced by source paper "a just and comprehensive strategy for using nlp to address online abuse"):
  with members:
    - "hybrid approaches to detect comments violating macro norms on reddit"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "towards non toxic landscapes automatic toxic comment detection using dnn"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "neural word decomposition models for abusive language detection"
    - "multi task deep neural networks for natural language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "all you need is love evading hate speech detection"
    - "challenges for toxic comment classification an in depth error analysis"
    - "neural character based composition models for abuse detection"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"):
  with members:
    - "offensive language analysis using deep learning architecture"
    - "amobee at semeval 2019 tasks 5 and 6 multiple choice cnn over contextual embedding"
    - "absit invidia verbo comparing deep learning methods for offensive language"
    - "combination of multiple deep learning architectures for offensive language detection in tweets"
    - "bidirectional recurrent models for offensive tweet classification"
    - "towards nlp with deep learning convolutional neural networks and recurrent neural networks for offensive language identification in social media"
    - "predicting the type and target of offensive posts in social media"
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media"

Connected through hyperedge HE-6 (induced by source paper "tackling online abuse a survey of automated abuse detection methods"):
  with members:
    - "predicting the type and target of offensive posts in social media"
    - "abusive language detection with graph convolutional networks"
    - "challenges for toxic comment classification an in depth error analysis"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "reducing gender bias in abusive language detection"
    - "ritual uh at trac 2018 shared task aggression identification"
    - "leveraging intra user and inter user representation learning for automated hate speech detection"
    - "automated hate speech detection and the problem of offensive language"

Connected through hyperedge HE-7 (induced by source paper "multilingual and multi aspect hate speech analysis"):
  with members:
    - "challenges in discriminating profanity from hate speech"
    - "examining a hate speech corpus for hate speech detection and popularity prediction"
    - "hierarchical cvae for fine grained hate speech classification"
    - "reducing gender bias in abusive language detection"
    - "hate lingo a target based linguistic analysis of hate speech in social media"
    - "mean birds detecting aggression and bullying on twitter"
    - "automated hate speech detection and the problem of offensive language"
    - "unsupervised machine translation using monolingual corpora only"

Connected through hyperedge HE-8 (induced by source paper "hatemonitors language agnostic abuse detection in social media"):
  with members:
    - "the enemy among us detecting hate speech with threats based othering language embeddings"
    - "a quantitative approach to understanding online antisemitism"
    - "cyber hate classification othering language and paragraph embedding"
    - "hierarchical cvae for fine grained hate speech classification"
    - "hateminers detecting hate speech against women"
    - "spread of hate speech in online social media"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "detecting weak and strong islamophobic hate speech on 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.CL(Computation and Language)