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 factoid question answering system for vietnamese"
  Abstract: In this paper, we describe the development of an end-to-end factoid question answering system for the Vietnamese language. This system combines both statistical models and ontology-based methods in a chain of processing modules to provide high-quality mappings from natural language text to entities. We present the challenges in the development of such an intelligent user interface for an isolating language like Vietnamese and show that techniques developed for inflectional languages cannot be applied "as is". Our question answering system can answer a wide range of general knowledge questions with promising accuracy on a test set.

Connected through hyperedge HE-1 (induced by source paper "a pilot study on multiple choice machine reading comprehension for vietnamese texts"):
  with members:
    - "vietnamese transition based dependency parsing with supertag features"
    - "semeval 2017 task 3 community question answering"
    - "korquad1 0 korean qa dataset for machine reading comprehension"
    - "error analysis for vietnamese named entity recognition on deep neural network models"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "sberquad russian reading comprehension dataset description and analysis"
    - "spoken squad a study of mitigating the impact of speech recognition errors on listening comprehension"
    - "lstm easy first dependency parsing with pre trained word embeddings and character level word embeddings in vietnamese"

Connected through hyperedge HE-2 (induced by source paper "emotion recognition for vietnamese social media text"):
  with members:
    - "vietnamese transition based dependency parsing with supertag features"
    - "deep learning versus traditional classifiers on vietnamese students feedback corpus"
    - "decision support with text based emotion recognition deep learning for affective computing"
    - "iest wassa 2018 implicit emotions shared task"
    - "from word segmentation to pos tagging for vietnamese"
    - "emotion intensities in tweets"
    - "convolutional neural networks for sentence classification"

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)