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: "predicting the type and target of offensive posts in social media"
  Abstract: As offensive content has become pervasive in social media, there has been much research in identifying potentially offensive messages. However, previous work on this topic did not consider the problem as a whole, but rather focused on detecting very specific types of offensive content, e.g., hate speech, cyberbulling, or cyber-aggression. In contrast, here we target several different kinds of offensive content. In particular, we model the task hierarchically, identifying the type and the target of offensive messages in social media. For this purpose, we complied the Offensive Language Identification Dataset (OLID), a new dataset with tweets annotated for offensive content using a fine-grained three-layer annotation scheme, which we make publicly available. We discuss the main similarities and differences between OLID and pre-existing datasets for hate speech identification, aggression detection, and similar tasks. We further experiment with and we compare the performance of different machine learning models on OLID.

Connected through hyperedge HE-1 (induced by source paper "offensive language identification in greek"):
  with members:
    - "offensive language and hate speech detection for danish"
    - "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"
    - "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"
    - "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"
    - "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 "social bias frames reasoning about social and power implications of language"):
  with members:
    - "talkdown a corpus for condescension detection in context"
    - "unifying human and statistical evaluation for natural language generation"
    - "model cards for model reporting"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "contextual affective analysis a case study of people portrayals in online metoo stories"
    - "cooperative generator discriminator networks for abstractive summarization with narrative flow"
    - "racial bias in hate speech and abusive language detection datasets"
    - "winogrande an adversarial winograd schema challenge at scale"

Connected through hyperedge HE-4 (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"
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media"
    - "ubc nlp at semeval 2019 task 6 ensemble learning of offensive content with enhanced training data"

Connected through hyperedge HE-5 (induced by source paper "tackling online abuse a survey of automated abuse detection methods"):
  with members:
    - "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"
    - "understanding abuse a typology of abusive language detection subtasks"
    - "automated hate speech detection and the problem of offensive language"

Connected through hyperedge HE-6 (induced by source paper "nlpr srpol at semeval 2019 task 6 and task 5 linguistically enhanced deep learning offensive sentence classifier"):
  with members:
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"
    - "all you need is love evading hate speech detection"
    - "on the strength of character language models for multilingual named entity recognition"
    - "challenges for toxic comment classification an in depth error analysis"
    - "a unified deep learning architecture for abuse detection"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "effective hate speech detection in twitter data using recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "identifying offensive posts and targeted offense from twitter"):
  with members:
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"
    - "challenges in discriminating profanity from hate speech"
    - "kiki kills identifying dangerous challenge videos from social media"
    - "mind your language abuse and offense detection for code switched languages"
    - "a multimodal approach to predict social media popularity"
    - "phramacovigilance exploring deep learning techniques for identifying mentions of medication intake from twitter"
    - "effective hate speech detection in twitter data using recurrent neural networks"
    - "understanding abuse a typology of abusive language detection subtasks"

Connected through hyperedge HE-8 (induced by source paper "um iu ling at semeval 2019 task 6 identifying offensive tweets using bert and svms"):
  with members:
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"
    - "challenges in discriminating profanity from hate speech"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "automated hate speech detection and the problem of offensive language"
    - "deep learning for hate speech detection in tweets"
    - "google s neural machine translation system bridging the gap between human and machine translation"

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)