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: "detecting hate speech in social media"
  Abstract: In this paper we examine methods to detect hate speech in social media, while distinguishing this from general profanity. We aim to establish lexical baselines for this task by applying supervised classification methods using a recently released dataset annotated for this purpose. As features, our system uses character n-grams, word n-grams and word skip-grams. We obtain results of 78% accuracy in identifying posts across three classes. Results demonstrate that the main challenge lies in discriminating profanity and hate speech from each other. A number of directions for future work are discussed.

Connected through hyperedge HE-1 (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"
    - "a dictionary based approach to racism detection in dutch social media"

Connected through hyperedge HE-2 (induced by source paper "developing a multilingual annotated corpus of misogyny and aggression"):
  with members:
    - "when a tweet is actually sexist a more comprehensive classification of different online harassment categories and the challenges in nlp"
    - "predicting the type and target of offensive posts in social media"
    - "challenges in discriminating profanity from hate speech"
    - "aggression annotated corpus of hindi english code mixed data"
    - "understanding abuse a typology of abusive language detection subtasks"
    - "automated hate speech detection and the problem of offensive language"
    - "characterizing linguistic attributes for automatic classification of intent based racist radicalized posts on tumblr micro blogging website"
    - "modeling trolling in social media conversations"

Connected through hyperedge HE-3 (induced by source paper "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"):
  with members:
    - "offensive language analysis using deep learning architecture"
    - "amobee at semeval 2019 tasks 5 and 6 multiple choice cnn over contextual embedding"
    - "absit invidia verbo comparing deep learning methods for offensive language"
    - "combination of multiple deep learning architectures for offensive language detection in tweets"
    - "bidirectional recurrent models for offensive tweet classification"
    - "towards nlp with deep learning convolutional neural networks and recurrent neural networks for offensive language identification in social media"
    - "predicting the type and target of offensive posts in social media"
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media"

Connected through hyperedge HE-4 (induced by source paper "exploring hate speech detection in multimodal publications"):
  with members:
    - "learning to learn from web data through deep semantic embeddings"
    - "recurrent fusion network for image captioning"
    - "peer to peer hate hate speech instigators and their targets"
    - "automated hate speech detection and the problem of offensive language"
    - "one step and two step classification for abusive language detection on twitter"
    - "improving text proposals for scene images with fully convolutional networks"
    - "a web of hate tackling hateful speech in online social spaces"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"

Connected through hyperedge HE-5 (induced by source paper "predicting the type and target of offensive posts in social media"):
  with members:
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"
    - "challenges in discriminating profanity from hate speech"
    - "understanding abuse a typology of abusive language detection subtasks"
    - "automated hate speech detection and the problem of offensive language"
    - "enriching word vectors with subword information"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-6 (induced by source paper "utfpr at semeval 2019 task 5 hate speech identification with recurrent neural networks"):
  with members:
    - "challenges in discriminating profanity from hate speech"
    - "hate lingo a target based linguistic analysis of hate speech in social media"
    - "understanding abuse a typology of abusive language detection subtasks"
    - "automated hate speech detection and the problem of offensive language"
    - "finding function in form compositional character models for open vocabulary word representation"

Connected through hyperedge HE-7 (induced by source paper "hate speech detection on vietnamese social media text using the bidirectional lstm model"):
  with members:
    - "learning word vectors for 157 languages"
    - "leveraging intra user and inter user representation learning for automated hate speech detection"
    - "automated hate speech detection and the problem of offensive language"
    - "detecting online hate speech using context aware models"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-8 (induced by source paper "hate speech in pixels detection of offensive memes towards automatic moderation"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multimodal social media analysis for gang violence prevention"
    - "very deep convolutional networks for large scale image recognition"

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)