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: "subword encoding in lattice lstm for chinese word segmentation"
  Abstract: We investigate a lattice LSTM network for Chinese word segmentation (CWS) to utilize words or subwords. It integrates the character sequence features with all subsequences information matched from a lexicon. The matched subsequences serve as information shortcut tunnels which link their start and end characters directly. Gated units are used to control the contribution of multiple input links. Through formula derivation and comparison, we show that the lattice LSTM is an extension of the standard LSTM with the ability to take multiple inputs. Previous lattice LSTM model takes word embeddings as the lexicon input, we prove that subword encoding can give the comparable performance and has the benefit of not relying on any external segmentor. The contribution of lattice LSTM comes from both lexicon and pretrained embeddings information, we find that the lexicon information contributes more than the pretrained embeddings information through controlled experiments. Our experiments show that the lattice structure with subword encoding gives competitive or better results with previous state-of-the-art methods on four segmentation benchmarks. Detailed analyses are conducted to compare the performance of word encoding and subword encoding in lattice LSTM. We also investigate the performance of lattice LSTM structure under different circumstances and when this model works or fails.

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"
    - "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"
    - "bag of words as target for neural machine translation"

Connected through hyperedge HE-2 (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"
    - "finding better subword segmentation for neural machine translation"
    - "neural based pinyin to character conversion with adaptive vocabulary"

Connected through hyperedge HE-3 (induced by source paper "is word segmentation necessary for deep learning of chinese representations"):
  with members:
    - "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"
    - "which encoding is the best for text classification in chinese english japanese and korean"

Connected through hyperedge HE-4 (induced by source paper "lattice cnns for matching based chinese question answering"):
  with members:
    - "dating documents using graph convolution networks"
    - "chinese ner using lattice lstm"
    - "a graph to sequence model for amr to text generation"
    - "cross sentence n ary relation extraction with graph lstms"
    - "improved neural relation detection for knowledge base question answering"
    - "matchzoo a toolkit for deep text matching"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "bilateral multi perspective matching for natural language sentences"

Connected through hyperedge HE-5 (induced by source paper "investigating self attention network for chinese word segmentation"):
  with members:
    - "improving cross domain"
    - "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"
    - "deep semantic role labeling with self attention"

Connected through hyperedge HE-6 (induced by source paper "toward fast and accurate neural chinese word segmentation with multi criteria learning"):
  with members:
    - "know what you don t know unanswerable questions for squad"
    - "coqa a conversational question answering challenge"
    - "switch lstms for multi criteria chinese word segmentation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "attention is all you need"
    - "neural word segmentation with rich pretraining"
    - "multi task domain adaptation for sequence tagging"

Connected through hyperedge HE-7 (induced by source paper "active learning for chinese word segmentation in medical text"):
  with members:
    - "a seq to seq transformer premised temporal convolutional network for chinese word segmentation"
    - "active learning with partial feedback"
    - "learning active learning from data"
    - "neural word segmentation learning for chinese"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-8 (induced by source paper "chinese spelling error detection using a fusion lattice lstm"):
  with members:
    - "chinese ner using lattice lstm"
    - "lstm a search space odyssey"
    - "adam a method for stochastic optimization"

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)