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: "hate speech detection a solved problem the challenging case of long tail on twitter"
  Abstract: In recent years, the increasing propagation of hate speech on social media and the urgent need for effective counter-measures have drawn significant investment from governments, companies, and empirical research. Despite a large number of emerging, scientific studies to address the problem, the performance of existing automated methods at identifying specific types of hate speech - as opposed to identifying non-hate -is still very unsatisfactory, and the reasons behind are poorly understood. This work undertakes the first in-depth analysis towards this problem and shows that, the very challenging nature of identifying hate speech on the social media is largely due to the extremely unbalanced presence of real hateful content in the typical datasets, and the lack of unique, discriminative features in such content, both causing them to reside in the 'long tail' of a dataset that is difficult to discover. To address this issue, we propose novel Deep Neural Network structures serving as effective feature extractors, and explore the usage of background information in the form of different word embeddings pre-trained from unlabelled corpora. We empirically evaluate our methods on the largest collection of hate speech datasets based on Twitter, and show that our methods can significantly outperform state of the art, as they are able to obtain a maximum improvement of between 4 and 16 percentage points (macro-average F1) depending on datasets.

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"
    - "convolutional neural networks for toxic comment classification"
    - "detecting hate speech and offensive language on twitter using machine learning an n gram and tfidf based approach"
    - "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 "multi label categorization of accounts of sexism using a neural framework"):
  with members:
    - "safecity understanding diverse forms of sexual harassment personal stories"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep learning for detecting cyberbullying across multiple social media platforms"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "deep learning for hate speech detection in tweets"
    - "recognizing explicit and implicit hate speech using a weakly supervised two path bootstrapping approach"

Connected through hyperedge HE-3 (induced by source paper "towards countering hate speech and personal attack in social media"):
  with members:
    - "a unified deep learning architecture for abuse detection"
    - "learning word vectors for 157 languages"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "universal language model fine tuning for text classification"
    - "degree based classification of harmful speech using twitter data"
    - "automated hate speech detection and the problem of offensive language"
    - "deep learning for hate speech detection in tweets"

Connected through hyperedge HE-4 (induced by source paper "attending the emotions to detect online abusive language"):
  with members:
    - "abusive language detection with graph convolutional networks"
    - "deep contextualized word representations"
    - "neural character based composition models for abuse detection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "automated hate speech detection and the problem of offensive language"
    - "deep learning for hate speech detection in tweets"
    - "ex machina personal attacks seen at scale"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "hate speech detection on vietnamese social media text using the bi gru lstm cnn model"):
  with members:
    - "understanding convolutional neural networks for text classification"
    - "learning word vectors for 157 languages"
    - "deep learning for hate speech detection in tweets"
    - "enriching word vectors with subword information"
    - "text classification improved by integrating bidirectional lstm with two dimensional max pooling"
    - "recurrent neural network for text classification with multi task learning"
    - "bidirectional lstm crf models for sequence tagging"
    - "named entity recognition with bidirectional lstm cnns"

Connected through hyperedge HE-6 (induced by source paper "drug drug interaction prediction based on knowledge graph embeddings and convolutional lstm network"):
  with members:
    - "detecting drug drug interactions using artificial neural networks and classic graph similarity measures"
    - "interaction embeddings for prediction and explanation in knowledge graphs"
    - "pytorch biggraph a large scale graph embedding system"
    - "predicting rich drug drug interactions via biomedical knowledge graphs and text jointly embedding"
    - "analysis of the impact of negative sampling on link prediction in knowledge graphs"
    - "complex embeddings for simple link prediction"
    - "knowlege graph embedding by flexible translation"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

Connected through hyperedge HE-7 (induced by source paper "challenges for toxic comment classification an in depth error analysis"):
  with members:
    - "automated hate speech detection and the problem of offensive language"
    - "deep learning for hate speech detection in tweets"
    - "detecting online hate speech using context aware models"
    - "enriching word vectors with subword information"
    - "ex machina personal attacks seen at scale"
    - "measuring the reliability of hate speech annotations the case of the european refugee crisis"

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)