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: "challenges for toxic comment classification an in depth error analysis"
  Abstract: Toxic comment classification has become an active research field with many recently proposed approaches. However, while these approaches address some of the task's challenges others still remain unsolved and directions for further research are needed. To this end, we compare different deep learning and shallow approaches on a new, large comment dataset and propose an ensemble that outperforms all individual models. Further, we validate our findings on a second dataset. The results of the ensemble enable us to perform an extensive error analysis, which reveals open challenges for state-of-the-art methods and directions towards pending future research. These challenges include missing paradigmatic context and inconsistent dataset labels.

Connected through hyperedge HE-1 (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"
    - "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"
    - "conversations gone awry detecting early signs of conversational failure"

Connected through hyperedge HE-2 (induced by source paper "read attend and comment a deep architecture for automatic news comment generation"):
  with members:
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "know what you don t know unanswerable questions for squad"
    - "automatic article commenting the task and dataset"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"
    - "s net from answer extraction to answer generation for machine reading comprehension"

Connected through hyperedge HE-3 (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"
    - "neural character based composition models for abuse detection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "understanding abuse a typology of abusive language detection subtasks"

Connected through hyperedge HE-4 (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"
    - "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"
    - "automated hate speech detection and the problem of offensive language"

Connected through hyperedge HE-5 (induced by source paper "nlpr srpol at semeval 2019 task 6 and task 5 linguistically enhanced deep learning offensive sentence classifier"):
  with members:
    - "predicting the type and target of offensive posts in social media"
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"
    - "all you need is love evading hate speech detection"
    - "on the strength of character language models for multilingual named entity recognition"
    - "a unified deep learning architecture for abuse detection"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "effective hate speech detection in twitter data using recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "towards robust toxic content classification"):
  with members:
    - "on adversarial examples for character level neural machine translation"
    - "textbugger generating adversarial text against real world applications"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "generating natural language adversarial examples"
    - "deep contextualized word representations"
    - "neural character based composition models for abuse detection"
    - "towards crafting text adversarial samples"
    - "attention is all you need"

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)