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: "measuring the reliability of hate speech annotations the case of the european refugee crisis"
  Abstract: Some users of social media are spreading racist, sexist, and otherwise hateful content. For the purpose of training a hate speech detection system, the reliability of the annotations is crucial, but there is no#R##N#universally agreed-upon definition. We collected potentially hateful messages and asked two groups of internet users to determine#R##N#whether they were hate speech or not, whether they should be banned or not and to rate their degree of offensiveness. One of the groups was shown a definition prior to completing the survey. We aimed to assess whether hate speech can be annotated reliably, and the extent to which existing definitions are in accordance with subjective ratings. Our results indicate that showing users a definition caused them to partially align their own opinion with the definition but did not improve reliability, which was very low overall. We conclude that the presence of hate speech should perhaps#R##N#not be considered a binary yes-or-no decision, and raters need more detailed instructions for the annotation.

Connected through hyperedge HE-1 (induced by source paper "noiserank unsupervised label noise reduction with dependence models"):
  with members:
    - "deep self learning from noisy labels"
    - "billion scale semi supervised learning for image classification"
    - "are anchor points really indispensable in label noise learning"
    - "an effective label noise model for dnn text classification"
    - "unsupervised label noise modeling and loss correction"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "joint optimization framework for learning with noisy labels"

Connected through hyperedge HE-2 (induced by source paper "offensive language identification in greek"):
  with members:
    - "offensive language and hate speech detection for danish"
    - "predicting the type and target of offensive posts in social media"
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"
    - "challenges in discriminating profanity from hate speech"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "understanding abuse a typology of abusive language detection subtasks"
    - "automated hate speech detection and the problem of offensive language"
    - "detecting hate speech in social media"

Connected through hyperedge HE-3 (induced by source paper "social bias frames reasoning about social and power implications of language"):
  with members:
    - "talkdown a corpus for condescension detection in context"
    - "unifying human and statistical evaluation for natural language generation"
    - "model cards for model reporting"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "contextual affective analysis a case study of people portrayals in online metoo stories"
    - "cooperative generator discriminator networks for abstractive summarization with narrative flow"
    - "predicting the type and target of offensive posts in social media"
    - "racial bias in hate speech and abusive language detection datasets"

Connected through hyperedge HE-4 (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"
    - "leveraging intra user and inter user representation learning for automated hate speech detection"
    - "understanding abuse a typology of abusive language detection subtasks"

Connected through hyperedge HE-5 (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"
    - "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"
    - "a web of hate tackling hateful speech in online social spaces"

Connected through hyperedge HE-6 (induced by source paper "conan counter narratives through nichesourcing a multilingual dataset of responses to fight online hate speech"):
  with members:
    - "bleaching text abstract features for cross lingual gender prediction"
    - "training millions of personalized dialogue agents"
    - "examining a hate speech corpus for hate speech detection and popularity prediction"
    - "hate speech dataset from a white supremacy forum"
    - "thou shalt not hate countering online hate speech"
    - "peer to peer hate hate speech instigators and their targets"
    - "cross lingual transfer learning for multilingual task oriented dialog"
    - "analyzing the hate and counter speech accounts on twitter"

Connected through hyperedge HE-7 (induced by source paper "multilingual and multi aspect hate speech analysis"):
  with members:
    - "challenges in discriminating profanity from hate speech"
    - "examining a hate speech corpus for hate speech detection and popularity prediction"
    - "hierarchical cvae for fine grained hate speech classification"
    - "reducing gender bias in abusive language detection"
    - "hate lingo a target based linguistic analysis of hate speech in social media"
    - "understanding abuse a typology of abusive language detection subtasks"
    - "mean birds detecting aggression and bullying on twitter"
    - "automated hate speech detection and the problem of offensive language"

Connected through hyperedge HE-8 (induced by source paper "hatemonitors language agnostic abuse detection in social media"):
  with members:
    - "the enemy among us detecting hate speech with threats based othering language embeddings"
    - "a quantitative approach to understanding online antisemitism"
    - "cyber hate classification othering language and paragraph embedding"
    - "hierarchical cvae for fine grained hate speech classification"
    - "hateminers detecting hate speech against women"
    - "spread of hate speech in online social media"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "detecting weak and strong islamophobic hate speech on social media"

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)