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: "a unified deep learning architecture for abuse detection"
  Abstract: Hate speech, offensive language, sexism, racism and other types of abusive behavior have become a common phenomenon in many online social media platforms. In recent years, such diverse abusive behaviors have been manifesting with increased frequency and levels of intensity. This is due to the openness and willingness of popular media platforms, such as Twitter and Facebook, to host content of sensitive or controversial topics. However, these platforms have not adequately addressed the problem of online abusive behavior, and their responsiveness to the effective detection and blocking of such inappropriate behavior remains limited. #R##N#In the present paper, we study this complex problem by following a more holistic approach, which considers the various aspects of abusive behavior. To make the approach tangible, we focus on Twitter data and analyze user and textual properties from different angles of abusive posting behavior. We propose a deep learning architecture, which utilizes a wide variety of available metadata, and combines it with automatically-extracted hidden patterns within the text of the tweets, to detect multiple abusive behavioral norms which are highly inter-related. We apply this unified architecture in a seamless, transparent fashion to detect different types of abusive behavior (hate speech, sexism vs. racism, bullying, sarcasm, etc.) without the need for any tuning of the model architecture for each task. We test the proposed approach with multiple datasets addressing different and multiple abusive behaviors on Twitter. Our results demonstrate that it largely outperforms the state-of-art methods (between 21 and 45\% improvement in AUC, depending on the dataset).

Connected through hyperedge HE-1 (induced by source paper "nlpr srpol at semeval 2019 task 6 and task 5 linguistically enhanced deep learning offensive sentence classifier"):
  with members:
    - "predicting the type and target of offensive posts in social media"
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"
    - "all you need is love evading hate speech detection"
    - "on the strength of character language models for multilingual named entity recognition"
    - "challenges for toxic comment classification an in depth error analysis"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "effective hate speech detection in twitter data using recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "towards integration of statistical hypothesis tests into deep neural networks"):
  with members:
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "joint embedding of words and labels for text classification"
    - "automated hate speech detection and the problem of offensive language"
    - "topic compositional neural language model"
    - "deconvolutional latent variable model for text sequence matching"
    - "convolutional sequence to sequence learning"
    - "bidirectional attention flow for machine comprehension"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-3 (induced by source paper "towards countering hate speech and personal attack in social media"):
  with members:
    - "hate speech detection a solved problem the challenging case of long tail on twitter"
    - "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 "a bert based transfer learning approach for hate speech detection in online social media"):
  with members:
    - "and we will fight for our race a measurement study of genetic testing conversations on reddit and 4chan"
    - "racial bias in hate speech and abusive language detection datasets"
    - "challenges in discriminating profanity from hate speech"
    - "the effect of extremist violence on hateful speech online"
    - "deep contextualized word representations"
    - "analyzing right wing youtube channels hate violence and discrimination"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "transfer learning for hate speech detection in social media"):
  with members:
    - "deep contextualized word representations"
    - "mean birds detecting aggression and bullying on twitter"
    - "automated hate speech detection and the problem of offensive language"
    - "attention is all you need"
    - "one step and two step classification for abusive language detection on twitter"
    - "ex machina personal attacks seen at scale"
    - "antisocial behavior in online discussion communities"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-6 (induced by source paper "bridging information security and environmental criminology research to better mitigate cybercrime"):
  with members:
    - "you know what to do proactive detection of youtube videos targeted by coordinated hate attacks"
    - "mean birds detecting aggression and bullying on twitter"
    - "what s in a name understanding profile name reuse on twitter"
    - "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"
    - "traveling the silk road a measurement analysis of a large anonymous online marketplace"

Connected through hyperedge HE-7 (induced by source paper "a quantitative approach to understanding online antisemitism"):
  with members:
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "automatic detection of online jihadist hate speech"
    - "mobilizing the trump train understanding collective action in a political trolling community"
    - "understanding web archiving services and their mis use on social media"
    - "peer to peer hate hate speech instigators and their targets"
    - "hate lingo a target based linguistic analysis of hate speech in social media"
    - "on the origins of memes by means of fringe web communities"
    - "automated hate speech detection and the problem of offensive language"

Connected through hyperedge HE-8 (induced by source paper "predictive embeddings for hate speech detection on twitter"):
  with members:
    - "automated hate speech detection and the problem of offensive language"

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)