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: "deep learning for user comment moderation"
  Abstract: Experimenting with a new dataset of 1.6M user comments from a Greek news portal and existing datasets of English Wikipedia comments, we show that an RNN outperforms the previous state of the art in moderation. A deep, classification-specific attention mechanism improves further the overall performance of the RNN. We also compare against a CNN and a word-list baseline, considering both fully automatic and semi-automatic moderation.

Connected through hyperedge HE-1 (induced by source paper "wac a corpus of wikipedia conversations for online abuse detection"):
  with members:
    - "conversational networks for automatic online moderation"
    - "trouble on the horizon forecasting the derailment of online conversations as they develop"
    - "abusive language detection in online conversations by combining content and graph based features"
    - "all you need is love evading hate speech detection"
    - "neural character based composition models for abuse detection"
    - "wikiconv a corpus of the complete conversational history of a large online collaborative community"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "ex machina personal attacks seen at scale"

Connected through hyperedge HE-2 (induced by source paper "learning invariant representations of social media users"):
  with members:
    - "hunting for troll comments in news community forums"
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"
    - "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"

Connected through hyperedge HE-3 (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"
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"
    - "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"

Connected through hyperedge HE-4 (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"
    - "one step and two step classification for abusive language detection on twitter"
    - "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-5 (induced by source paper "conversational networks for automatic online moderation"):
  with members:
    - "impact of content features for automatic online abuse detection"
    - "one step and two step classification for abusive language detection on twitter"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "graph based features for automatic online abuse detection"
    - "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 "abusive language detection in online conversations by combining content and graph based features"):
  with members:
    - "conversational networks for automatic online moderation"
    - "neural character based composition models for abuse detection"
    - "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"
    - "ex machina personal attacks seen at scale"

Connected through hyperedge HE-7 (induced by source paper "extractive adversarial networks high recall explanations for identifying personal attacks in social media posts"):
  with members:
    - "detecting deceptive reviews using generative adversarial networks"
    - "explaining explanations an overview of interpretability of machine learning"
    - "a survey of methods for explaining black box models"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "binary generative adversarial networks for image retrieval"
    - "explaining recurrent neural network predictions in sentiment analysis"
    - "rationalization a neural machine translation approach to generating natural language explanations"
    - "deceiving google s perspective api built for detecting toxic comments"

Connected through hyperedge HE-8 (induced by source paper "ntua slp at semeval 2018 task 1 predicting affective content in tweets with deep attentive rnns and transfer learning"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "charagram embedding words and sentences via character n grams"
    - "adam a method for stochastic optimization"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "learning phrase representations using rnn encoder decoder for statistical 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)