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: "neural chinese named entity recognition via cnn lstm crf and joint training with word segmentation"
  Abstract: Chinese named entity recognition (CNER) is an important task in Chinese natural language processing field. However, CNER is very challenging since Chinese entity names are highly context-dependent. In addition, Chinese texts lack delimiters to separate words, making it difficult to identify the boundary of entities. Besides, the training data for CNER in many domains is usually insufficient, and annotating enough training data for CNER is very expensive and time-consuming. In this paper, we propose a neural approach for CNER. First, we introduce a CNN-LSTM-CRF neural architecture to capture both local and long-distance contexts for CNER. Second, we propose a unified framework to jointly train CNER and word segmentation models in order to enhance the ability of CNER model in identifying entity boundaries. Third, we introduce an automatic method to generate pseudo labeled samples from existing labeled data which can enrich the training data. Experiments on two benchmark datasets show that our approach can effectively improve the performance of Chinese named entity recognition, especially when training data is insufficient.

Connected through hyperedge HE-1 (induced by source paper "porous lattice based transformer encoder for chinese ner"):
  with members:
    - "lattice transformer for speech translation"
    - "lattice based transformer encoder for neural machine translation"
    - "self attentional models for lattice inputs"
    - "neural collective entity linking based on recurrent random walk network learning"
    - "chinese ner using lattice lstm"
    - "modeling localness for self attention networks"
    - "convolutional self attention network"
    - "combining discrete and neural features for sequence labeling"

Connected through hyperedge HE-2 (induced by source paper "on the vietnamese name entity recognition a deep learning method approach"):
  with members:
    - "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"
    - "nnvlp a neural network based vietnamese language processing toolkit"
    - "vietnamese named entity recognition using token regular expressions and bidirectional inference"

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)