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: "antisocial behavior in online discussion communities"
  Abstract: User contributions in the form of posts, comments, and votes are essential to the success of online communities. However, allowing user participation also invites undesirable behavior such as trolling. In this paper, we characterize antisocial behavior in three large online discussion communities by analyzing users who were banned from these communities. We find that such users tend to concentrate their efforts in a small number of threads, are more likely to post irrelevantly, and are more successful at garnering responses from other users. Studying the evolution of these users from the moment they join a community up to when they get banned, we find that not only do they write worse than other users over time, but they also become increasingly less tolerated by the community. Further, we discover that antisocial behavior is exacerbated when community feedback is overly harsh. Our analysis also reveals distinct groups of users with different levels of antisocial behavior that can change over time. We use these insights to identify antisocial users early on, a task of high practical importance to community maintainers.

Connected through hyperedge HE-1 (induced by source paper "empirical analysis of multi task learning for reducing model bias in toxic comment detection"):
  with members:
    - "nuanced metrics for measuring unintended bias with real data for text classification"
    - "racial bias in hate speech and abusive language detection datasets"
    - "machine learning suites for online toxicity detection"
    - "challenges for toxic comment classification an in depth error analysis"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "learning tasks for multitask learning heterogenous patient populations in the icu"
    - "sarcasm analysis using conversation context"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"

Connected through hyperedge HE-2 (induced by source paper "trajectories of blocked community members redemption recidivism and departure"):
  with members:
    - "mind your pov convergence of articles and editors towards wikipedia s neutrality norm"
    - "forecasting the presence and intensity of hostility on instagram using linguistic and social features"
    - "the effect of extremist violence on hateful speech online"
    - "community interaction and conflict on the web"
    - "wikiconv a corpus of the complete conversational history of a large online collaborative community"
    - "the view from the other side the border between controversial speech and harassment on kotaku in action"
    - "conversations gone awry detecting early signs of conversational failure"
    - "characterizing and detecting hateful users on twitter"

Connected through hyperedge HE-3 (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-4 (induced by source paper "regression under human assistance"):
  with members:
    - "adaptive hard thresholding for near optimal consistent robust regression"
    - "selectivenet a deep neural network with an integrated reject option"
    - "the algorithmic automation problem prediction triage and human effort"
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "direct uncertainty prediction for medical second opinions"
    - "non submodular function maximization subject to a matroid constraint with applications"
    - "bias reduced uncertainty estimation for deep neural classifiers"
    - "active regression via linear sample sparsification"

Connected through hyperedge HE-5 (induced by source paper "race and religion in online abuse towards uk politicians working paper"):
  with members:
    - "partisanship propaganda and post truth politics quantifying impact in online debate"
    - "online abuse of uk mps from 2015 to 2019 working paper"
    - "tweeting mps digital engagement between citizens and members of parliament in the uk"
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "adversarial removal of demographic attributes from text data"
    - "reducing gender bias in abusive language detection"
    - "gender bias in coreference resolution evaluation and debiasing methods"
    - "semantics derived automatically from language corpora contain human like biases"

Connected through hyperedge HE-6 (induced by source paper "online abuse of uk mps from 2015 to 2019 working paper"):
  with members:
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "a large scale crowdsourced analysis of abuse against women journalists and politicians on twitter"
    - "adversarial removal of demographic attributes from text data"
    - "reducing gender bias in abusive language detection"
    - "gender bias in coreference resolution evaluation and debiasing methods"
    - "on fairness and calibration"
    - "semantics derived automatically from language corpora contain human like biases"
    - "equality of opportunity in supervised learning"

Connected through hyperedge HE-7 (induced by source paper "content removal as a moderation strategy compliance and other outcomes in the changemyview community"):
  with members:
    - "trajectories of blocked community members redemption recidivism and departure"
    - "trouble on the horizon forecasting the derailment of online conversations as they develop"
    - "mind your pov convergence of articles and editors towards wikipedia s neutrality norm"
    - "forecasting the presence and intensity of hostility on instagram using linguistic and social features"
    - "reducing gender bias in abusive language detection"
    - "conversations gone awry detecting early signs of conversational failure"
    - "a warm welcome matters the link between social feedback and weight loss in r loseit"
    - "measuring offensive speech in online political discourse"

Connected through hyperedge HE-8 (induced by source paper "estimating causal effects of tone in online debates"):
  with members:
    - "before name calling dynamics and triggers of ad hominem fallacies in web argumentation"
    - "topic independent identification of agreement and disagreement in social media dialogue"
    - "really well apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "emoticons vs emojis on twitter a causal inference approach"
    - "using summarization to discover argument facets in online idealogical dialog"
    - "the effect of wording on message propagation topic and author controlled natural experiments on twitter"
    - "distributed representations of words and phrases and their compositionality"

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)