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: "challenges in discriminating profanity from hate speech"
  Abstract: In this study we approach the problem of distinguishing general profanity from hate speech in social media, something which has not been widely considered. Using a new dataset annotated specifically for this task, we employ supervised classification along with a set of features that includes n-grams, skip-grams and clustering-based word representations. We apply approaches based on single classifiers as well as more advanced ensemble classifiers and stacked generalization, achieving the best result of 80% accuracy for this 3-class classification task. Analysis of the results reveals that discriminating hate speech and profanity is not a simple task, which may require features that capture a deeper understanding of the text not always possible with surface n-grams. The variability of gold labels in the annotated data, due to differences in the subjective adjudications of the annotators, is also an issue. Other directions for future work are discussed.

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"
    - "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"
    - "predicting the type and target of offensive posts in social media"
    - "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 "temporal effects of unmoderated hate speech in gab"):
  with members:
    - "auditing radicalization pathways on youtube"
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "all you need is love evading hate speech detection"
    - "the effect of extremist violence on hateful speech online"
    - "hate speech dataset from a white supremacy forum"
    - "thou shalt not hate countering online hate speech"
    - "spread of hate speech in online social media"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"

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"
    - "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-5 (induced by source paper "multilingual and multi aspect hate speech analysis"):
  with members:
    - "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"
    - "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"
    - "unsupervised machine translation using monolingual corpora only"

Connected through hyperedge HE-6 (induced by source paper "identifying offensive posts and targeted offense from twitter"):
  with members:
    - "predicting the type and target of offensive posts in social media"
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"
    - "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-7 (induced by source paper "a bert based transfer learning approach for hate speech detection in online social media"):
  with members:
    - "and we will fight for our race a measurement study of genetic testing conversations on reddit and 4chan"
    - "racial bias in hate speech and abusive language detection datasets"
    - "a unified deep learning architecture for abuse detection"
    - "the effect of extremist violence on hateful speech online"
    - "deep contextualized word representations"
    - "analyzing right wing youtube channels hate violence and discrimination"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"

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:
    - "predicting the type and target of offensive posts in social media"
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"
    - "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)