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 feature rich vietnamese named entity recognition model"
  Abstract: In this paper, we present a feature-based named-entity recognition (NER) model that achieves the start-of-the-art accuracy for Vietnamese language. We combine word, word-shape features, PoS, chunk, Brown-cluster-based features, and word-embedding-based features in the Conditional Random Fields (CRF) model. We also explore the effects of word segmentation, PoS tagging, and chunking results of many popular Vietnamese NLP toolkits on the accuracy of the proposed feature-based NER model. Up to now, our work is the first work that systematically performs an extrinsic evaluation of basic Vietnamese NLP toolkits on the downstream NER task. Experimental results show that while automatically-generated word segmentation is useful, PoS and chunking information generated by Vietnamese NLP tools does not show their benefits for the proposed feature-based NER model.

Connected through hyperedge HE-1 (induced by source paper "on the vietnamese name entity recognition a deep learning method approach"):
  with members:
    - "neural chinese named entity recognition via cnn lstm crf and joint training with word segmentation"
    - "deep contextualized word representations"
    - "a feature based model for nested named entity recognition at vlsp 2018 ner evaluation campaign"
    - "semi supervised sequence modeling with cross view training"
    - "attentive neural network for named entity recognition in vietnamese"
    - "application of a hybrid bi lstm crf model to the task of russian named entity recognition"
    - "nnvlp a neural network based vietnamese language processing toolkit"
    - "vietnamese named entity recognition using token regular expressions and bidirectional inference"

Connected through hyperedge HE-2 (induced by source paper "neural sequence labeling for vietnamese pos tagging and ner"):
  with members:
    - "the importance of automatic syntactic features in vietnamese named entity recognition"
    - "nnvlp a neural network based vietnamese language processing toolkit"
    - "vietnamese named entity recognition using token regular expressions and bidirectional inference"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "lexicon infused phrase embeddings for named entity resolution"

Connected through hyperedge HE-3 (induced by source paper "attentive neural network for named entity recognition in vietnamese"):
  with members:
    - "vncorenlp a vietnamese natural language processing toolkit"
    - "a feature based model for nested named entity recognition at vlsp 2018 ner evaluation campaign"
    - "the importance of automatic syntactic features in vietnamese named entity recognition"
    - "nnvlp a neural network based vietnamese language processing toolkit"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "a feature based model for nested named entity recognition at vlsp 2018 ner evaluation campaign"):
  with members:
    - "a fast and accurate vietnamese word segmenter"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "introduction to the conll 2002 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)