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: "nnvlp a neural network based vietnamese language processing toolkit"
  Abstract: This paper demonstrates neural network-based toolkit namely NNVLP for essential Vietnamese language processing tasks including part-of-speech (POS) tagging, chunking, named entity recognition (NER). Our toolkit is a combination of bidirectional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network (CNN), Conditional Random Field (CRF), using pre-trained word embeddings as input, which achieves state-of-the-art results on these three tasks. We provide both API and web demo for this toolkit.

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"
    - "a feature rich vietnamese named entity recognition model"
    - "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"
    - "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:
    - "a feature rich vietnamese named entity recognition model"
    - "the importance of automatic syntactic features in vietnamese named entity recognition"
    - "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 "a feature rich vietnamese named entity recognition model"):
  with members:
    - "a fast and accurate vietnamese word segmenter"
    - "the importance of automatic syntactic features in vietnamese named entity recognition"
    - "from word segmentation to pos tagging for vietnamese"
    - "vietnamese named entity recognition using token regular expressions and bidirectional inference"
    - "named entity recognition with bidirectional lstm cnns"
    - "introduction to the conll 2003 shared task language independent named entity recognition"
    - "introduction to the conll 2002 shared task language independent named entity recognition"

Connected through hyperedge HE-4 (induced by source paper "attentive neural network for named entity recognition in vietnamese"):
  with members:
    - "a feature rich vietnamese named entity recognition model"
    - "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"
    - "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-5 (induced by source paper "a factoid question answering system for vietnamese"):
  with members:
    - "building a semantic role labelling system for vietnamese"
    - "ripple down rules for question answering"
    - "vietnamese named entity recognition using token regular expressions and bidirectional inference"
    - "open question answering with weakly supervised embedding models"
    - "question answering with subgraph embeddings"

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)