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: "neural character based composition models for abuse detection"
  Abstract: The advent of social media in recent years has fed into some highly undesirable phenomena such as proliferation of offensive language, hate speech, sexist remarks, etc. on the Internet. In light of this, there have been several efforts to automate the detection and moderation of such abusive content. However, deliberate obfuscation of words by users to evade detection poses a serious challenge to the effectiveness of these efforts. The current state of the art approaches to abusive language detection, based on recurrent neural networks, do not explicitly address this problem and resort to a generic OOV (out of vocabulary) embedding for unseen words. However, in using a single embedding for all unseen words we lose the ability to distinguish between obfuscated and non-obfuscated or rare words. In this paper, we address this problem by designing a model that can compose embeddings for unseen words. We experimentally demonstrate that our approach significantly advances the current state of the art in abuse detection on datasets from two different domains, namely Twitter and Wikipedia talk page.

Connected through hyperedge HE-1 (induced by source paper "wac a corpus of wikipedia conversations for online abuse detection"):
  with members:
    - "conversational networks for automatic online moderation"
    - "trouble on the horizon forecasting the derailment of online conversations as they develop"
    - "abusive language detection in online conversations by combining content and graph based features"
    - "all you need is love evading hate speech detection"
    - "wikiconv a corpus of the complete conversational history of a large online collaborative community"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "deep learning for user comment moderation"
    - "ex machina personal attacks seen at scale"

Connected through hyperedge HE-2 (induced by source paper "a gentle introduction to deep learning for graphs"):
  with members:
    - "deep tree transductions a short survey"
    - "gmnn graph markov neural networks"
    - "a model to search for synthesizable molecules"
    - "graph u nets"
    - "a fair comparison of graph neural networks for graph classification"
    - "self attention graph pooling"
    - "deep graph library towards efficient and scalable deep learning on graphs"
    - "fast and deep graph neural networks"

Connected through hyperedge HE-3 (induced by source paper "towards non toxic landscapes automatic toxic comment detection using dnn"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "neural word decomposition models for abusive language detection"
    - "multi task deep neural networks for natural language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "all you need is love evading hate speech detection"
    - "challenges for toxic comment classification an in depth error analysis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "understanding abuse a typology of abusive language detection subtasks"

Connected through hyperedge HE-4 (induced by source paper "neural word decomposition models for abusive language detection"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"
    - "deep contextualized word representations"
    - "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"
    - "a web of hate tackling hateful speech in online social spaces"
    - "deep learning for user comment moderation"

Connected through hyperedge HE-5 (induced by source paper "towards robust toxic content classification"):
  with members:
    - "on adversarial examples for character level neural machine translation"
    - "challenges for toxic comment classification an in depth error analysis"
    - "textbugger generating adversarial text against real world applications"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "generating natural language adversarial examples"
    - "deep contextualized word representations"
    - "towards crafting text adversarial samples"
    - "attention is all you need"

Connected through hyperedge HE-6 (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"
    - "hate speech detection a solved problem the challenging case of long tail on twitter"
    - "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-7 (induced by source paper "abusive language detection in online conversations by combining content and graph based features"):
  with members:
    - "conversational networks for automatic online moderation"
    - "impact of content features for automatic online abuse detection"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "graph based features for automatic online abuse detection"
    - "deep learning for user comment moderation"
    - "ex machina personal attacks seen at scale"

Connected through hyperedge HE-8 (induced by source paper "abusive language detection with graph convolutional networks"):
  with members:
    - "leveraging intra user and inter user representation learning for automated hate speech 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"

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)