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: "large scale crowdsourcing and characterization of twitter abusive behavior"
  Abstract: In recent years, offensive, abusive and hateful language, sexism, racism and other types of aggressive and cyberbullying behavior have been manifesting with increased frequency, and in many online social media platforms. In fact, past scientific work focused on studying these forms in popular media, such as Facebook and Twitter. #R##N#Building on such work, we present an 8-month study of the various forms of abusive behavior on Twitter, in a holistic fashion. Departing from past work, we examine a wide variety of labeling schemes, which cover different forms of abusive behavior, at the same time. We propose an incremental and iterative methodology, that utilizes the power of crowdsourcing to annotate a large scale collection of tweets with a set of abuse-related labels. In fact, by applying our methodology including statistical analysis for label merging or elimination, we identify a reduced but robust set of labels. Finally, we offer a first overview and findings of our collected and annotated dataset of 100 thousand tweets, which we make publicly available for further scientific exploration.

Connected through hyperedge HE-1 (induced by source paper "multilingual twitter corpus and baselines for evaluating demographic bias in hate speech recognition"):
  with members:
    - "mitigating gender bias in natural language processing literature review"
    - "huggingface s transformers state of the art natural language processing"
    - "nuanced metrics for measuring unintended bias with real data for text classification"
    - "racial bias in hate speech and abusive language detection datasets"
    - "how to fine tune bert for text classification"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "counterfactual fairness in text classification through robustness"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "angry birds flock together aggression propagation on social media"):
  with members:
    - "detecting cyberbullying and cyberaggression in social media"
    - "mean birds detecting aggression and bullying on twitter"
    - "automated hate speech detection and the problem of offensive language"
    - "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"
    - "the social world of content abusers in community question answering"
    - "opinion maximization in social networks"

Connected through hyperedge HE-3 (induced by source paper "raiders of the lost kek 3 5 years of augmented 4chan posts from the politically incorrect board"):
  with members:
    - "and we will fight for our race a measurement study of genetic testing conversations on reddit and 4chan"
    - "you know what to do proactive detection of youtube videos targeted by coordinated hate attacks"
    - "whatsapp doc a first look at whatsapp public group data"
    - "on the origins of memes by means of fringe web communities"
    - "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"

Connected through hyperedge HE-4 (induced by source paper "the pushshift telegram dataset"):
  with members:
    - "nela gt 2018 a large multi labelled news dataset for the study of misinformation in news articles"
    - "can whatsapp counter misinformation by limiting message forwarding"
    - "whatsapp doc a first look at whatsapp public group data"

Connected through hyperedge HE-5 (induced by source paper "mitigating gender bias in natural language processing literature review"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "model cards for model reporting"
    - "black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings"
    - "gender bias in contextualized word embeddings"
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "mind the gap a balanced corpus of gendered ambiguous pronouns"
    - "assessing gender bias in machine translation a case study with google translate"
    - "women also snowboard overcoming bias in captioning models"

Connected through hyperedge HE-6 (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-7 (induced by source paper "you shall know a user by the company it keeps dynamic representations for social media users in nlp"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "abusive language detection with graph convolutional networks"
    - "representing social media users for sarcasm detection"
    - "cascade contextual sarcasm detection in online discussion forums"
    - "assessing state of the art sentiment models on state of the art sentiment datasets"
    - "attention is all you need"
    - "a decomposable attention model for natural language inference"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-8 (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"
    - "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"
    - "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.SI(Social and Information Networks)