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: "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"
  Abstract: We present the results and the main findings of SemEval-2019 Task 6 on Identifying and Categorizing Offensive Language in Social Media (OffensEval). The task was based on a new dataset, the Offensive Language Identification Dataset (OLID), which contains over 14,000 English tweets. It featured three sub-tasks. In sub-task A, the goal was to discriminate between offensive and non-offensive posts. In sub-task B, the focus was on the type of offensive content in the post. Finally, in sub-task C, systems had to detect the target of the offensive posts. OffensEval attracted a large number of participants and it was one of the most popular tasks in SemEval-2019. In total, about 800 teams signed up to participate in the task, and 115 of them submitted results, which we present and analyze in this report.

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"
    - "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 "offensive language detection a comparative analysis"):
  with members:
    - "enriching word vectors with subword information"
    - "gurls a least squares library for supervised learning"

Connected through hyperedge HE-3 (induced by source paper "learning invariant representations of social media users"):
  with members:
    - "hunting for troll comments in news community forums"
    - "deep learning is robust to massive label noise"
    - "arcface additive angular margin loss for deep face recognition"
    - "deep contextualized word representations"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "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"
    - "automated hate speech detection and the problem of offensive language"

Connected through hyperedge HE-4 (induced by source paper "nlpr srpol at semeval 2019 task 6 and task 5 linguistically enhanced deep learning offensive sentence classifier"):
  with members:
    - "predicting the type and target of offensive posts in social media"
    - "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-5 (induced by source paper "neural word decomposition models for abusive language detection"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "deep contextualized word representations"
    - "neural character based composition models for abuse detection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "automated hate speech detection and the problem of offensive language"
    - "deep learning for hate speech detection in tweets"
    - "a web of hate tackling hateful speech in online social spaces"
    - "deep learning for user comment moderation"

Connected through hyperedge HE-6 (induced by source paper "does bert make any sense interpretable word sense disambiguation with contextualized embeddings"):
  with members:
    - "sense vocabulary compression through the semantic knowledge of wordnet for neural word sense disambiguation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "roberta a robustly optimized bert pretraining approach"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "improving the coverage and the generalization ability of neural word sense disambiguation through hypernymy and hyponymy relationships"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (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"
    - "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:
    - "predicting the type and target of offensive posts in social media"
    - "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)