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: "all you need is love evading hate speech detection"
  Abstract: With the spread of social networks and their unfortunate use for hate speech, automatic detection of the latter has become a pressing problem. In this paper, we reproduce seven state-of-the-art hate speech detection models from prior work, and show that they perform well only when tested on the same type of data they were trained on. Based on these results, we argue that for successful hate speech detection, model architecture is less important than the type of data and labeling criteria. We further show that all proposed detection techniques are brittle against adversaries who can (automatically) insert typos, change word boundaries or add innocuous words to the original hate speech. A combination of these methods is also effective against Google Perspective -- a cutting-edge solution from industry. Our experiments demonstrate that adversarial training does not completely mitigate the attacks, and using character-level features makes the models systematically more attack-resistant than using word-level features.

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"
    - "neural character based composition models for abuse 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 "text analysis in adversarial settings does deception leave a stylistic trace"):
  with members:
    - "hunting for troll comments in news community forums"
    - "a survey on natural language processing for fake news detection"
    - "style transfer through back translation"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "stay on topic generating context specific fake restaurant reviews"
    - "toward controlled generation of text"

Connected through hyperedge HE-3 (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"
    - "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"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"

Connected through hyperedge HE-4 (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"
    - "challenges for toxic comment classification an in depth error analysis"
    - "neural character based composition models for abuse detection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "understanding abuse a typology of abusive language detection subtasks"

Connected through hyperedge HE-5 (induced by source paper "detecting and reducing bias in a high stakes domain"):
  with members:
    - "fine grained sentiment analysis with faithful attention"
    - "detecting gang involved escalation on social media using context"
    - "pathologies of neural models make interpretations difficult"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "generating natural language adversarial examples"
    - "deep contextualized word representations"
    - "deriving machine attention from human rationales"

Connected through hyperedge HE-6 (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"
    - "on the strength of character language models for multilingual named entity recognition"
    - "challenges for toxic comment classification an in depth error analysis"
    - "a unified deep learning architecture for abuse detection"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "effective hate speech detection in twitter data using recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "build it break it fix it for dialogue safety robustness from adversarial human attack"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"
    - "large scale gan training for high fidelity natural image synthesis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "automated hate speech detection and the problem of offensive language"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "ethical challenges in data driven dialogue systems"

Connected through hyperedge HE-8 (induced by source paper "manipulation of perceived politeness in a web based email discourse through a malicious browser extension"):
  with members:
    - "social engineering in a post phishing era ambient tactical deception attacks"
    - "sorry ambient tactical deception via malware based social engineering"
    - "defending against phishing attacks taxonomy of methods current issues and future directions"

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)