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: "convolutional neural networks for toxic comment classification"
  Abstract: Flood of information is produced in a daily basis through the global Internet usage arising from the on-line interactive communications among users. While this situation contributes significantly to the quality of human life, unfortunately it involves enormous dangers, since on-line texts with high toxicity can cause personal attacks, on-line harassment and bullying behaviors. This has triggered both industrial and research community in the last few years while there are several tries to identify an efficient model for on-line toxic comment prediction. However, these steps are still in their infancy and new approaches and frameworks are required. On parallel, the data explosion that appears constantly, makes the construction of new machine learning computational tools for managing this information, an imperative need. Thankfully advances in hardware, cloud computing and big data management allow the development of Deep Learning approaches appearing very promising performance so far. For text classification in particular the use of Convolutional Neural Networks (CNN) have recently been proposed approaching text analytics in a modern manner emphasizing in the structure of words in a document. In this work, we employ this approach to discover toxic comments in a large pool of documents provided by a current Kaggle's competition regarding Wikipedia's talk page edits. To justify this decision we choose to compare CNNs against the traditional bag-of-words approach for text analysis combined with a selection of algorithms proven to be very effective in text classification. The reported results provide enough evidence that CNN enhance toxic comment classification reinforcing research interest towards this direction.

Connected through hyperedge HE-1 (induced by source paper "comparison between traditional machine learning models and neural network models for vietnamese hate speech detection"):
  with members:
    - "emotion recognition for vietnamese social media text"
    - "job prediction from deep neural network models to applications"
    - "vais hate speech detection system a deep learning based approach for system combination"
    - "hate speech detection on vietnamese social media text using the bidirectional lstm model"
    - "detecting hate speech and offensive language on twitter using machine learning an n gram and tfidf based approach"
    - "hate speech detection a solved problem the challenging case of long tail on twitter"
    - "learning word vectors for 157 languages"
    - "automated hate speech detection and the problem of offensive language"

Connected through hyperedge HE-2 (induced by source paper "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"):
  with members:
    - "offensive language analysis using deep learning architecture"
    - "amobee at semeval 2019 tasks 5 and 6 multiple choice cnn over contextual embedding"
    - "absit invidia verbo comparing deep learning methods for offensive language"
    - "combination of multiple deep learning architectures for offensive language detection in tweets"
    - "bidirectional recurrent models for offensive tweet classification"
    - "towards nlp with deep learning convolutional neural networks and recurrent neural networks for offensive language identification in social media"
    - "predicting the type and target of offensive posts in social media"
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media"

Connected through hyperedge HE-3 (induced by source paper "detect toxic content to improve online conversations"):
  with members:
    - "advances in pre training distributed word representations"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-4 (induced by source paper "machine learning suites for online toxicity detection"):
  with members:
    - "all you need is love evading hate speech detection"
    - "is preprocessing of text really worth your time for online comment classification"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "deep learning for user comment moderation"

Connected through hyperedge HE-5 (induced by source paper "is preprocessing of text really worth your time for online comment classification"):
  with members:
    - "fasttext zip compressing text classification models"
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"
    - "smote synthetic minority over sampling technique"

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)