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 learning for chinese"
  Abstract: Most previous approaches to Chinese word segmentation formalize this problem as a character-based sequence labeling task where only contextual information within fixed sized local windows and simple interactions between adjacent tags can be captured. In this paper, we propose a novel neural framework which thoroughly eliminates context windows and can utilize complete segmentation history. Our model employs a gated combination neural network over characters to produce distributed representations of word candidates, which are then given to a long short-term memory (LSTM) language scoring model. Experiments on the benchmark datasets show that without the help of feature engineering as most existing approaches, our models achieve competitive or better performances with previous state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "integrating boundary assembling into a dnn framework for named entity recognition in chinese social media text"):
  with members:
    - "chinese ner using lattice lstm"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "learning word segmentation representations to improve named entity recognition for chinese social media"
    - "f score driven max margin neural network for named entity recognition in chinese social media"
    - "improving named entity recognition for chinese social media with word segmentation representation learning"
    - "neural architectures for named entity recognition"
    - "named entity recognition with bidirectional lstm cnns"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (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-3 (induced by source paper "lattice based transformer encoder for neural machine translation"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "chinese word segmentation another decade review 2007 2017"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "revisiting character based neural machine translation with capacity and compression"
    - "nict s neural and statistical machine translation systems for the wmt18 news translation task"
    - "chinese ner using lattice lstm"
    - "subword encoding in lattice lstm for chinese word segmentation"
    - "finding better subword segmentation for neural machine translation"

Connected through hyperedge HE-4 (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-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"
    - "neural word segmentation with rich pretraining"

Connected through hyperedge HE-7 (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"
    - "universal word segmentation implementation and interpretation"
    - "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"
    - "bag of tricks for efficient text classification"

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"
    - "neural word segmentation with rich pretraining"

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)