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: "deep learning for hate speech detection in tweets"
  Abstract: Hate speech detection on Twitter is critical for applications like controversial event extraction, building AI chatterbots, content recommendation, and sentiment analysis. We define this task as being able to classify a tweet as racist, sexist or neither. The complexity of the natural language constructs makes this task very challenging. We perform extensive experiments with multiple deep learning architectures to learn semantic word embeddings to handle this complexity. Our experiments on a benchmark dataset of 16K annotated tweets show that such deep learning methods outperform state-of-the-art char/word n-gram methods by ~18 F1 points.

Connected through hyperedge HE-1 (induced by source paper "ties temporal interaction embeddings for enhancing social media integrity at facebook"):
  with members:
    - "predicting dynamic embedding trajectory in temporal interaction networks"
    - "a survey of the recent architectures of deep convolutional neural networks"
    - "pytorch biggraph a large scale graph embedding system"
    - "poised spotting twitter spam off the beaten paths"
    - "attention is all you need"
    - "in a world that counts clustering and detecting fake social engagement at scale"
    - "a critical review of recurrent neural networks for sequence learning"

Connected through hyperedge HE-2 (induced by source paper "fog computing on constrained devices paving the way for the future iot"):
  with members:
    - "the internet of things fog and cloud continuum integration and challenges"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-3 (induced by source paper "text analysis in adversarial settings does deception leave a stylistic trace"):
  with members:
    - "hunting for troll comments in news community forums"
    - "all you need is love evading hate speech detection"
    - "a survey on natural language processing for fake news detection"
    - "style transfer through back translation"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "stay on topic generating context specific fake restaurant reviews"

Connected through hyperedge HE-4 (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-5 (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-6 (induced by source paper "deep sentiment sentiment analysis using ensemble of cnn and bi lstm models"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "mtbi identification from diffusion mr images using bag of adversarial visual features"
    - "automatic question answering using a deep similarity neural network"
    - "mask r cnn"
    - "deep reinforcement learning for dialogue generation"
    - "ssd single shot multibox detector"
    - "the darpa twitter bot challenge"
    - "a neural attention model for abstractive sentence summarization"

Connected through hyperedge HE-7 (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-8 (induced by source paper "tackling online abuse a survey of automated abuse detection methods"):
  with members:
    - "predicting the type and target of offensive posts in social media"
    - "abusive language detection with graph convolutional networks"
    - "challenges for toxic comment classification an in depth error analysis"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "reducing gender bias in abusive language detection"
    - "ritual uh at trac 2018 shared task aggression identification"
    - "leveraging intra user and inter user representation learning for automated hate speech detection"
    - "understanding abuse a typology of abusive language detection subtasks"

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)