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: "universal word segmentation implementation and interpretation"
  Abstract: Word segmentation is a low-level NLP task that is non-trivial for a considerable number of languages. In this paper, we present a sequence tagging framework and apply it to word segmentation for a wide range of languages with different writing systems and typological characteristics. Additionally, we investigate the correlations between various typological factors and word segmentation accuracy. The experimental results indicate that segmentation accuracy is positively related to word boundary markers and negatively to the number of unique non-segmental terms. Based on the analysis, we design a small set of language-specific settings and extensively evaluate the segmentation system on the Universal Dependencies datasets. Our model obtains state-of-the-art accuracies on all the UD languages. It performs substantially better on languages that are non-trivial to segment, such as Chinese, Japanese, Arabic and Hebrew, when compared to previous work.

Connected through hyperedge HE-1 (induced by source paper "char rnn and active learning for hashtag segmentation"):
  with members:
    - "plusemo2vec at semeval 2018 task 1 exploiting emotion knowledge from emoji and hashtags"
    - "an end to end neural natural language interface for databases"
    - "deep active learning for named entity recognition"
    - "a gap based framework for chinese word segmentation via very deep convolutional networks"
    - "attention is all you need"
    - "fast and accurate neural word segmentation for chinese"
    - "neural word segmentation learning for chinese"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-2 (induced by source paper "segmentation free compositional n gram embedding"):
  with members:
    - "generalizing word embeddings using bag of subwords"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "construction of a japanese word similarity dataset"
    - "bpemb tokenization free pre trained subword embeddings in 275 languages"
    - "neural word segmentation with rich pretraining"
    - "enriching word vectors with subword information"
    - "charagram embedding words and sentences via character n grams"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-3 (induced by source paper "a characterwise windowed approach to hebrew morphological segmentation"):
  with members:
    - "neural word segmentation learning for chinese"
    - "tensorflow a system for large scale machine learning"
    - "polyglot ner massive multilingual named entity recognition"
    - "a universal part of speech tagset"
    - "unsupervised discovery of morphemes"

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)