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: "improving multilingual named entity recognition with wikipedia entity type mapping"
  Abstract: The state-of-the-art named entity recognition (NER) systems are statistical machine learning models that have strong generalization capability (i.e., can recognize unseen entities that do not appear in training data) based on lexical and contextual information. However, such a model could still make mistakes if its features favor a wrong entity type. In this paper, we utilize Wikipedia as an open knowledge base to improve multilingual NER systems. Central to our approach is the construction of high-accuracy, high-coverage multilingual Wikipedia entity type mappings. These mappings are built from weakly annotated data and can be extended to new languages with no human annotation or language-dependent knowledge involved. Based on these mappings, we develop several approaches to improve an NER system. We evaluate the performance of the approaches via experiments on NER systems trained for 6 languages. Experimental results show that the proposed approaches are effective in improving the accuracy of such systems on unseen entities, especially when a system is applied to a new domain or it is trained with little training data (up to 18.3 F1 score improvement).

Connected through hyperedge HE-1 (induced by source paper "irony detection in a multilingual context"):
  with members:
    - "a corpus of english hindi code mixed tweets for sarcasm detection"
    - "bilingual sentiment embeddings joint projection of sentiment across languages"
    - "unsupervised cross lingual information retrieval using monolingual data only"
    - "learning word vectors for 157 languages"
    - "unsupervised cross lingual word embedding by multilingual neural language models"
    - "unsupervised neural machine translation"
    - "word translation without parallel data"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-2 (induced by source paper "flexner a flexible lstm cnn stack framework for named entity recognition"):
  with members:
    - "gsn a graph structured network for multi party dialogues"
    - "design challenges and misconceptions in neural sequence labeling"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "a discourse level named entity recognition and relation extraction dataset for chinese literature text"
    - "neural system combination for machine translation"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"

Connected through hyperedge HE-3 (induced by source paper "open named entity modeling from embedding distribution"):
  with members:
    - "deep contextualized word representations"
    - "weakly supervised cross lingual named entity recognition via effective annotation and representation projection"
    - "word translation without parallel data"
    - "neural architectures for named entity recognition"
    - "cross lingual pseudo projected expectation regularization for weakly supervised learning"
    - "distributed representations of words and phrases and their compositionality"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised cross lingual named entity recognition via effective annotation and representation projection"):
  with members:
    - "neural architectures for named entity recognition"
    - "polyglot ner massive multilingual named entity recognition"
    - "efficient estimation of word representations in vector space"
    - "exploiting similarities among languages for machine translation"
    - "introduction to the conll 2003 shared task language independent named entity 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)