human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "identifying offensive posts and targeted offense from twitter"):
  Abstract: In this paper we present our approach and the system description for Sub-task A and Sub Task B of SemEval 2019 Task 6: Identifying and Categorizing Offensive Language in Social Media. Sub-task A involves identifying if a given tweet is offensive or not, and Sub Task B involves detecting if an offensive tweet is targeted towards someone (group or an individual). Our models for Sub-task A is based on an ensemble of Convolutional Neural Network, Bidirectional LSTM with attention, and Bidirectional LSTM + Bidirectional GRU, whereas for Sub-task B, we rely on a set of heuristics derived from the training data and manual observation. We provide detailed analysis of the results obtained using the trained models. Our team ranked 5th out of 103 participants in Sub-task A, achieving a macro F1 score of 0.807, and ranked 8th out of 75 participants in Sub Task B achieving a macro F1 of 0.695.

Includes members:
  Member "predicting the type and target of offensive posts in social media":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "offensive language identification in greek")
  Member "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "offensive language identification in greek")
  Member "challenges in discriminating profanity from hate speech":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "offensive language identification in greek")
  Member "kiki kills identifying dangerous challenge videos from social media":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "suggestion mining from online reviews using ulmfit")
  Member "mind your language abuse and offense detection for code switched languages":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hope speech detection a computational analysis of the voice of peace")
  Member "a multimodal approach to predict social media popularity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "user aware folk popularity rank user popularity based tag recommendation that can enhance social popularity")
  Member "phramacovigilance exploring deep learning techniques for identifying mentions of medication intake from twitter":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "suggestion mining from online reviews using ulmfit")
  Member "effective hate speech detection in twitter data using recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "nlpr srpol at semeval 2019 task 6 and task 5 linguistically enhanced deep learning offensive sentence classifier")

Question: Please tell me which class the source paper 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)