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 word segmentation with rich pretraining"
  Abstract: Neural word segmentation research has benefited from large-scale raw texts by leveraging them for pretraining character and word embeddings. On the other hand, statistical segmentation research has exploited richer sources of external information, such as punctuation, automatic segmentation and POS. We investigate the effectiveness of a range of external training sources for neural word segmentation by building a modular segmentation model, pretraining the most important submodule using rich external sources. Results show that such pretraining significantly improves the model, leading to accuracies competitive to the best methods on six benchmarks.

Connected through hyperedge HE-1 (induced by source paper "glyce glyph vectors for chinese character representations"):
  with members:
    - "toward fast and accurate neural chinese word segmentation with multi criteria learning"
    - "chinese ner using lattice lstm"
    - "subword encoding in lattice lstm for chinese word segmentation"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "deep contextualized word representations"
    - "state of the art chinese word segmentation with bi lstms"
    - "prior knowledge integration for neural machine translation using posterior regularization"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "can ner convolutional attention network for chinese named entity recognition"):
  with members:
    - "grn gated relation network to enhance convolutional neural network for named entity recognition"
    - "analogical reasoning on chinese morphological and semantic relations"
    - "chinese ner using lattice lstm"
    - "design challenges and misconceptions in neural sequence labeling"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adversarial learning for chinese ner from crowd annotations"
    - "combining discrete and neural features for sequence labeling"

Connected through hyperedge HE-3 (induced by source paper "is word segmentation necessary for deep learning of chinese representations"):
  with members:
    - "subword encoding in lattice lstm for chinese word segmentation"
    - "prior knowledge integration for neural machine translation using posterior regularization"
    - "bag of words as target for neural machine translation"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "attention is all you need"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "modeling source syntax for neural machine translation"
    - "fast and accurate neural word segmentation for chinese"

Connected through hyperedge HE-4 (induced by source paper "porous lattice based transformer encoder for chinese ner"):
  with members:
    - "neural chinese named entity recognition via cnn lstm crf and joint training with word segmentation"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "investigating self attention network for chinese word segmentation"):
  with members:
    - "improving cross domain"
    - "subword encoding in lattice lstm for chinese word segmentation"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "constituency parsing with a self attentive encoder"
    - "linguistically informed self attention for semantic role labeling"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "attention is all you need for chinese word segmentation"):
  with members:
    - "self attention with relative position representations"
    - "switch lstms for multi criteria chinese word segmentation"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "attention is all you need"
    - "convolutional neural network with word embeddings for chinese word segmentation"
    - "fast and accurate neural word segmentation for chinese"
    - "a structured self attentive sentence embedding"
    - "dag based long short term memory for neural word segmentation"

Connected through hyperedge HE-7 (induced by source paper "a pilot study for chinese sql semantic parsing"):
  with members:
    - "improving semantic parsing for task oriented dialog"
    - "towards complex text to sql in cross domain database with intermediate representation"
    - "is word segmentation necessary for deep learning of chinese representations"
    - "grammar based neural text to sql generation"
    - "representing schema structure with graph neural networks for text to sql parsing"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "chinese ner using lattice lstm"
    - "sqlnet generating structured queries from natural language without reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "bert meets chinese word segmentation"):
  with members:
    - "chinese word segmentation another decade review 2007 2017"
    - "deep contextualized word representations"
    - "state of the art chinese word segmentation with bi lstms"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "convolutional neural network with word embeddings for chinese word segmentation"
    - "fast and accurate neural word segmentation for chinese"
    - "exploring segment representations for neural segmentation models"

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)