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 "comparison between traditional machine learning models and neural network models for vietnamese hate speech detection"):
  Abstract: Hate-speech detection on social network language has become one of the main researching fields recently due to the spreading of social networks like Facebook and Twitter. In Vietnam, the threat of offensive and harassment cause bad impacts for online user. The VLSP - Shared task about Hate Speech Detection on social networks showed many proposed approaches for detecting whatever comment is clean or not. However, this problem still needs further researching. Consequently, we compare traditional machine learning and deep learning on a large dataset about the user's comments on social network in Vietnamese and find out what is the advantage and disadvantage of each model by comparing their accuracy on F1-score, then we pick two models in which has highest accuracy in traditional machine learning models and deep neural models respectively. Next, we compare these two models capable of predicting the right label by referencing their confusion matrices and considering the advantages and disadvantages of each model. Finally, from the comparison result, we propose our ensemble method that concentrates the abilities of traditional methods and deep learning methods.

Includes members:
  Member "emotion recognition for vietnamese social media text":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "job prediction from deep neural network models to applications")
  Member "job prediction from deep neural network models to applications":
    also connected through other hyperedges:
      (none)
  Member "vais hate speech detection system a deep learning based approach for system combination":
    also connected through other hyperedges:
      (none)
  Member "hate speech detection on vietnamese social media text using the bidirectional lstm model":
    also connected through other hyperedges:
      (none)
  Member "convolutional neural networks for toxic comment classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval")
  Member "detecting hate speech and offensive language on twitter using machine learning an n gram and tfidf based approach":
    also connected through other hyperedges:
      (none)
  Member "hate speech detection a solved problem the challenging case of long tail on twitter":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi label categorization of accounts of sexism using a neural framework")
  Member "learning word vectors for 157 languages":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")

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)