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: "thou shalt not hate countering online hate speech"
  Abstract: Hate content in social media is ever-increasing. While Facebook, Twitter, Google have attempted to take several steps to tackle the hateful content, they have mostly been unsuccessful. Counterspeech is seen as an effective way of tackling the online hate without any harm to the freedom of speech. Thus, an alternative strategy for these platforms could be to promote counterspeech as a defense against hate content. However, in order to have a successful promotion of such counterspeech, one has to have a deep understanding of its dynamics in the online world. Lack of carefully curated data largely inhibits such understanding. In this paper, we create and release the first ever dataset for counterspeech using comments from YouTube. The data contains 13,924 manually annotated comments where the labels indicate whether a comment is a counterspeech or not. This data allows us to perform a rigorous measurement study characterizing the linguistic structure of counterspeech for the first time. This analysis results in various interesting insights such as: the counterspeech comments receive much more likes as compared to the non-counterspeech comments, for certain communities majority of the non-counterspeech comments tend to be hate speech, the different types of counterspeech are not all equally effective and the language choice of users posting counterspeech is largely different from those posting non-counterspeech as revealed by a detailed psycholinguistic analysis. Finally, we build a set of machine learning models that are able to automatically detect counterspeech in YouTube videos with an F1-score of 0.71. We also build multilabel models that can detect different types of counterspeech in a comment with an F1-score of 0.60.

Connected through hyperedge HE-1 (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"
    - "spread of hate speech in online social media"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"

Connected through hyperedge HE-2 (induced by source paper "a just and comprehensive strategy for using nlp to address online abuse"):
  with members:
    - "hybrid approaches to detect comments violating macro norms on reddit"
    - "machine learning suites for online toxicity detection"
    - "forecasting the presence and intensity of hostility on instagram using linguistic and social features"
    - "a survey on natural language processing for fake news detection"
    - "style transfer through back translation"
    - "extractive adversarial networks high recall explanations for identifying personal attacks in social media posts"
    - "fighting offensive language on social media with unsupervised text style transfer"
    - "conversations gone awry detecting early signs of conversational failure"

Connected through hyperedge HE-3 (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"
    - "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"
    - "automated hate speech detection and the problem of offensive language"

Connected through hyperedge HE-4 (induced by source paper "spread of hate speech in online social media"):
  with members:
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "a quantitative approach to understanding online antisemitism"
    - "the effect of extremist violence on hateful speech online"
    - "inside the right leaning echo chambers characterizing gab an unmoderated social system"
    - "hierarchical cvae for fine grained hate speech classification"
    - "hateminers detecting hate speech against women"
    - "analyzing right wing youtube channels hate violence and discrimination"
    - "peer to peer hate hate speech instigators and their targets"

Connected through hyperedge HE-5 (induced by source paper "analyzing the hate and counter speech accounts on twitter"):
  with members:
    - "inside the right leaning echo chambers characterizing gab an unmoderated social system"
    - "peer to peer hate hate speech instigators and their targets"
    - "like sheep among wolves characterizing hateful users on twitter"
    - "hate lingo a target based linguistic analysis of hate speech in social media"
    - "characterizing and detecting hateful users on twitter"
    - "automated hate speech detection and the problem of offensive language"
    - "measuring gamergate a tale of hate sexism and bullying"
    - "deep learning for hate speech detection in tweets"

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)