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: "mind your language abuse and offense detection for code switched languages"
  Abstract: In multilingual societies like the Indian subcontinent, use of code-switched languages is much popular and convenient for the users. In this paper, we study offense and abuse detection in the code-switched pair of Hindi and English (i.e. Hinglish), the pair that is the most spoken. The task is made difficult due to non-fixed grammar, vocabulary, semantics and spellings of Hinglish language. We apply transfer learning and make a LSTM based model for hate speech classification. This model surpasses the performance shown by the current best models to establish itself as the state-of-the-art in the unexplored domain of Hinglish offensive text classification.We also release our model and the embeddings trained for research purposes

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "hope speech detection a computational analysis of the voice of peace"):
  with members:
    - "something s brewing early prediction of controversy causing posts from discussion features"
    - "polyglot contextual representations improve crosslingual transfer"
    - "kashmir a computational analysis of the voice of peace"
    - "low resource parsing with crosslingual contextualized representations"
    - "polyglot semantic role labeling"
    - "forecasting the presence and intensity of hostility on instagram using linguistic and social features"
    - "learning to generate reviews and discovering sentiment"
    - "automated hate speech detection and the problem of offensive language"

Connected through hyperedge HE-3 (induced by source paper "suggestion mining from online reviews using ulmfit"):
  with members:
    - "kiki kills identifying dangerous challenge videos from social media"
    - "a multimodal approach to predict social media popularity"
    - "phramacovigilance exploring deep learning techniques for identifying mentions of medication intake from twitter"
    - "open domain suggestion mining problem definition and datasets"

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)