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: "leveraging intra user and inter user representation learning for automated hate speech detection"
  Abstract: Hate speech detection is a critical, yet challenging problem in Natural Language Processing (NLP). Despite the existence of numerous studies dedicated to the development of NLP hate speech detection approaches, the accuracy is still poor. The central problem is that social media posts are short and noisy, and most existing hate speech detection solutions take each post as an isolated input instance, which is likely to yield high false positive and negative rates. In this paper, we radically improve automated hate speech detection by presenting a novel model that leverages intra-user and inter-user representation learning for robust hate speech detection on Twitter. In addition to the target Tweet, we collect and analyze the user's historical posts to model intra-user Tweet representations. To suppress the noise in a single Tweet, we also model the similar Tweets posted by all other users with reinforced inter-user representation learning techniques. Experimentally, we show that leveraging these two representations can significantly improve the f-score of a strong bidirectional LSTM baseline model by 10.1%.

Connected through hyperedge HE-1 (induced by source paper "temporal effects of unmoderated hate speech in gab"):
  with members:
    - "auditing radicalization pathways on youtube"
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "all you need is love evading hate speech detection"
    - "challenges in discriminating profanity from hate speech"
    - "the effect of extremist violence on hateful speech online"
    - "hate speech dataset from a white supremacy forum"
    - "thou shalt not hate countering online hate speech"
    - "spread of hate speech in online social media"

Connected through hyperedge HE-2 (induced by source paper "tackling online abuse a survey of automated abuse detection methods"):
  with members:
    - "predicting the type and target of offensive posts in social media"
    - "abusive language detection with graph convolutional networks"
    - "challenges for toxic comment classification an in depth error analysis"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "reducing gender bias in abusive language detection"
    - "ritual uh at trac 2018 shared task aggression identification"
    - "understanding abuse a typology of abusive language detection subtasks"
    - "automated hate speech detection and the problem of offensive language"

Connected through hyperedge HE-3 (induced by source paper "a benchmark dataset for learning to intervene in online hate speech"):
  with members:
    - "hierarchical cvae for fine grained hate speech classification"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "peer to peer hate hate speech instigators and their targets"
    - "hate lingo a target based linguistic analysis of hate speech in social media"
    - "mean birds detecting aggression and bullying on twitter"
    - "automated hate speech detection and the problem of offensive language"
    - "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-4 (induced by source paper "abusive language detection with graph convolutional networks"):
  with members:
    - "neural character based composition models for abuse detection"
    - "improved abusive comment moderation with user embeddings"
    - "node2vec scalable feature learning for networks"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "hate speech detection on vietnamese social media text using the bidirectional lstm model"):
  with members:
    - "learning word vectors for 157 languages"
    - "automated hate speech detection and the problem of offensive language"
    - "detecting hate speech in social media"
    - "detecting online hate speech using context aware models"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-6 (induced by source paper "neural character based composition models for abuse detection"):
  with members:
    - "automated hate speech detection and the problem of offensive language"
    - "deep learning for hate speech detection in tweets"
    - "mimicking word embeddings using subword rnns"
    - "deep learning for user comment moderation"
    - "enriching word vectors with subword information"
    - "ex machina personal attacks seen at scale"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "character aware neural language models"

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)