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: "a web of hate tackling hateful speech in online social spaces"
  Abstract: Online social platforms are beset with hateful speech - content that expresses hatred for a person or group of people. Such content can frighten, intimidate, or silence platform users, and some of it can inspire other users to commit violence. Despite widespread recognition of the problems posed by such content, reliable solutions even for detecting hateful speech are lacking. In the present work, we establish why keyword-based methods are insufficient for detection. We then propose an approach to detecting hateful speech that uses content produced by self-identifying hateful communities as training data. Our approach bypasses the expensive annotation process often required to train keyword systems and performs well across several established platforms, making substantial improvements over current state-of-the-art approaches.

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"
    - "thou shalt not hate countering online hate speech"
    - "spread of hate speech in online social media"

Connected through hyperedge HE-2 (induced by source paper "towards non toxic landscapes automatic toxic comment detection using dnn"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "neural word decomposition models for abusive language detection"
    - "multi task deep neural networks for natural language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "all you need is love evading hate speech detection"
    - "challenges for toxic comment classification an in depth error analysis"
    - "neural character based composition models for abuse detection"
    - "bert pre training of deep bidirectional transformers for language understanding"

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"
    - "deep learning for user comment moderation"

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"
    - "detecting hate speech in social media"
    - "one step and two step classification for abusive language detection on twitter"
    - "improving text proposals for scene images with fully convolutional networks"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"

Connected through hyperedge HE-5 (induced by source paper "towards countering hate speech and personal attack in social media"):
  with members:
    - "a unified deep learning architecture for abuse detection"
    - "hate speech detection a solved problem the challenging case of long tail on twitter"
    - "learning word vectors for 157 languages"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "universal language model fine tuning for text classification"
    - "degree based classification of harmful speech using twitter data"
    - "automated hate speech detection and the problem of offensive language"

Connected through hyperedge HE-6 (induced by source paper "firsthand opiates abuse on social media monitoring geospatial patterns of interest through a digital cohort"):
  with members:
    - "the anatomy of reddit an overview of academic research"
    - "caveat emptor computational social science large scale missing data in a widely published reddit corpus"
    - "twitter for sparking a movement reddit for sharing the moment metoo through the lens of social media"
    - "the impact of crowds on news engagement a reddit case study"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "query expansion with locally trained word embeddings"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "navigating the massive world of reddit using backbone networks to map user interests in social media"

Connected through hyperedge HE-7 (induced by source paper "a quantitative approach to understanding online antisemitism"):
  with members:
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "a unified deep learning architecture for abuse detection"
    - "automatic detection of online jihadist hate speech"
    - "mobilizing the trump train understanding collective action in a political trolling community"
    - "understanding web archiving services and their mis use on social media"
    - "peer to peer hate hate speech instigators and their targets"
    - "hate lingo a target based linguistic analysis of hate speech in social media"
    - "on the origins of memes by means of fringe web communities"

Connected through hyperedge HE-8 (induced by source paper "caveat emptor computational social science large scale missing data in a widely published reddit corpus"):
  with members:
    - "the rise and fall of network stars analyzing 2 5 million graphs to reveal how high degree vertices emerge over time"
    - "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"
    - "the impact of crowds on news engagement a reddit case study"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "analyzing complex network user arrival patterns and their effect on network topologies"
    - "averaging gone wrong using time aware analyses to better understand behavior"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "what do vegans do in their spare time latent interest detection in multi community networks"

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)