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: "one step and two step classification for abusive language detection on twitter"
  Abstract: Automatic abusive language detection is a difficult but important task for online social media. Our research explores a two-step approach of performing classification on abusive language and then classifying into specific types and compares it with one-step approach of doing one multi-class classification for detecting sexist and racist languages. With a public English Twitter corpus of 20 thousand tweets in the type of sexism and racism, our approach shows a promising performance of 0.827 F-measure by using HybridCNN in one-step and 0.824 F-measure by using logistic regression in two-steps.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "exploring hate speech detection in multimodal publications"):
  with members:
    - "learning to learn from web data through deep semantic embeddings"
    - "recurrent fusion network for image captioning"
    - "peer to peer hate hate speech instigators and their targets"
    - "automated hate speech detection and the problem of offensive language"
    - "detecting hate speech in social media"
    - "improving text proposals for scene images with fully convolutional networks"
    - "a web of hate tackling hateful speech in online social spaces"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"

Connected through hyperedge HE-3 (induced by source paper "author profiling for hate speech detection"):
  with members:
    - "automated hate speech detection and the problem of offensive language"
    - "deep learning for hate speech detection in tweets"
    - "deep learning for user comment moderation"
    - "utcnn a deep learning model of stance classificationon on social media text"
    - "node2vec scalable feature learning for networks"
    - "ex machina personal attacks seen at scale"
    - "modelling context with user embeddings for sarcasm detection in social media"
    - "toward socially infused information extraction embedding authors mentions and entities"

Connected through hyperedge HE-4 (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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "conversational networks for automatic online moderation"):
  with members:
    - "impact of content features for automatic online abuse detection"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "graph based features for automatic online abuse detection"
    - "deep learning for user comment moderation"
    - "a community role approach to assess social capitalists visibility in the twitter network"
    - "antisocial behavior in online discussion communities"
    - "quantifying controversy in social media"
    - "identifying the community roles of social capitalists in the twitter network"

Connected through hyperedge HE-6 (induced by source paper "transfer learning for hate speech detection in social media"):
  with members:
    - "a unified deep learning architecture for abuse detection"
    - "deep contextualized word representations"
    - "mean birds detecting aggression and bullying on twitter"
    - "automated hate speech detection and the problem of offensive language"
    - "attention is all you need"
    - "ex machina personal attacks seen at scale"
    - "antisocial behavior in online discussion communities"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-7 (induced by source paper "attention based method for categorizing different types of online harassment language"):
  with members:
    - "how is your mood when writing sexist tweets detecting the emotion type and intensity of emotion using natural language processing techniques"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "effective hate speech detection in twitter data using recurrent neural networks"
    - "improving neural machine translation models with monolingual data"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"
    - "efficient object localization using convolutional networks"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-8 (induced by source paper "rating for parents predicting children suitability rating for movies based on language of the movies"):
  with members:
    - "automated hate speech detection and the problem of offensive language"
    - "adam a method for stochastic optimization"
    - "towards understanding cyberbullying behavior in a semi anonymous social network"
    - "neural machine translation by jointly learning to align and translate"
    - "from once upon a time to happily ever after tracking emotions in novels and fairy tales"

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)