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: "on the strength of character language models for multilingual named entity recognition"
  Abstract: Character-level patterns have been widely used as features in English Named Entity Recognition (NER) systems. However, to date there has been no direct investigation of the inherent differences between name and non-name tokens in text, nor whether this property holds across multiple languages. This paper analyzes the capabilities of corpus-agnostic Character-level Language Models (CLMs) in the binary task of distinguishing name tokens from non-name tokens. We demonstrate that CLMs provide a simple and powerful model for capturing these differences, identifying named entity tokens in a diverse set of languages at close to the performance of full NER systems. Moreover, by adding very simple CLM-based features we can significantly improve the performance of an off-the-shelf NER system for multiple languages.

Connected through hyperedge HE-1 (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"
    - "challenges for toxic comment classification an in depth error analysis"
    - "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-2 (induced by source paper "pretrained language model transfer on neural named entity recognition in indonesian conversational texts"):
  with members:
    - "deep contextualized word representations"
    - "deep rnns encode soft hierarchical syntax"
    - "colorless green recurrent networks dream hierarchically"
    - "universal language model fine tuning for text classification"
    - "still not systematic after all these years on the compositional skills of sequence to sequence recurrent networks"
    - "semi supervised multitask learning for sequence labeling"
    - "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"
    - "transfer learning for named entity recognition with neural networks"

Connected through hyperedge HE-3 (induced by source paper "cross lingual transfer learning for multilingual task oriented dialog"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "polyglot contextual representations improve crosslingual transfer"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "pytext a seamless path from nlp research to production"
    - "parameter sharing between dependency parsers for related languages"
    - "zero shot cross lingual classification using multilingual neural machine translation"
    - "xnli evaluating cross lingual sentence representations"

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)