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: "effective hate speech detection in twitter data using recurrent neural networks"
  Abstract: This paper addresses the important problem of discerning hateful content in social media. We propose a detection scheme that is an ensemble of Recurrent Neural Network (RNN) classifiers, and it incorporates various features associated with user-related information, such as the users’ tendency towards racism or sexism. This data is fed as input to the above classifiers along with the word frequency vectors derived from the textual content. We evaluate our approach on a publicly available corpus of 16k tweets, and the results demonstrate its effectiveness in comparison to existing state-of-the-art solutions. More specifically, our scheme can successfully distinguish racism and sexism messages from normal text, and achieve higher classification quality than current state-of-the-art algorithms.

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 "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"
    - "understanding abuse a typology of abusive language detection subtasks"

Connected through hyperedge HE-3 (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"
    - "one step and two step classification for abusive language detection on twitter"
    - "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-4 (induced by source paper "ubc nlp at semeval 2019 task 6 ensemble learning of offensive content with enhanced training data"):
  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"
    - "automated hate speech detection and the problem of offensive language"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-5 (induced by source paper "a unified deep learning architecture for abuse detection"):
  with members:
    - "challenges in discriminating profanity from hate speech"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "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"
    - "deep learning for hate speech detection in tweets"
    - "one step and two step classification for abusive language detection on twitter"
    - "bag of tricks for efficient text classification"

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)