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: "recognizing explicit and implicit hate speech using a weakly supervised two path bootstrapping approach"
  Abstract: In the wake of a polarizing election, social media is laden with hateful content. To address various limitations of supervised hate speech classification methods including corpus bias and huge cost of annotation, we propose a weakly supervised two-path bootstrapping approach for an online hate speech detection model leveraging large-scale unlabeled data. This system significantly outperforms hate speech detection systems that are trained in a supervised manner using manually annotated data. Applying this model on a large quantity of tweets collected before, after, and on election day reveals motivations and patterns of inflammatory language.

Connected through hyperedge HE-1 (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"
    - "thou shalt not hate countering online hate speech"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "a benchmark dataset for learning to intervene in online hate speech"):
  with members:
    - "hierarchical cvae for fine grained hate speech classification"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "peer to peer hate hate speech instigators and their targets"
    - "hate lingo a target based linguistic analysis of hate speech in social media"
    - "leveraging intra user and inter user representation learning for automated hate speech detection"
    - "mean birds detecting aggression and bullying on twitter"
    - "automated hate speech detection and the problem of offensive language"
    - "deep learning for hate speech detection in tweets"

Connected through hyperedge HE-3 (induced by source paper "and we will fight for our race a measurement study of genetic testing conversations on reddit and 4chan"):
  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"
    - "mobilizing the trump train understanding collective action in a political trolling community"
    - "analyzing right wing youtube channels hate violence and discrimination"
    - "23andme confirms i m super white analyzing twitter discourse on genetic testing"
    - "on the origins of memes by means of fringe web communities"
    - "characterizing and detecting hateful users on twitter"

Connected through hyperedge HE-4 (induced by source paper "learning to decipher hate symbols"):
  with members:
    - "generating sentences by editing prototypes"
    - "learning to explain non standard english words and phrases"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "definition modeling learning to define word embeddings in natural language"
    - "ex machina personal attacks seen at scale"
    - "variational neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "learning to understand phrases by embedding the dictionary"

Connected through hyperedge HE-5 (induced by source paper "multi label categorization of accounts of sexism using a neural framework"):
  with members:
    - "safecity understanding diverse forms of sexual harassment personal stories"
    - "deep contextualized word representations"
    - "hate speech detection a solved problem the challenging case of long tail on twitter"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep learning for detecting cyberbullying across multiple social media platforms"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "deep learning for hate speech detection in tweets"

Connected through hyperedge HE-6 (induced by source paper "cyber hate classification othering language and paragraph embedding"):
  with members:
    - "automated hate speech detection and the problem of offensive language"
    - "detecting hate speech in social media"
    - "deep learning for hate speech detection in tweets"
    - "a web of hate tackling hateful speech in online social spaces"
    - "ex machina personal attacks seen at scale"
    - "analyzing the targets of hate in online social media"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "hierarchical cvae for fine grained hate speech classification"):
  with members:
    - "automated hate speech detection and the problem of offensive language"
    - "deep learning for hate speech detection in tweets"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "variational neural machine translation"
    - "improved semantic representations from tree structured long short term memory networks"
    - "autoencoding beyond pixels using a learned similarity metric"
    - "a c lstm neural network for text classification"
    - "effective use of word order for text categorization with convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "when politicians talk about politics identifying political tweets of brazilian congressmen"):
  with members:
    - "portuguese word embeddings evaluating on word analogies and natural language tasks"
    - "how many political parties should brazil have a data driven method to assess and reduce fragmentation in multi party political systems"
    - "efficient estimation of word representations in vector space"

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)