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: "chinese ner using lattice lstm"
  Abstract: We investigate a lattice-structured LSTM model for Chinese NER, which encodes a sequence of input characters as well as all potential words that match a lexicon. Compared with character-based methods, our model explicitly leverages word and word sequence information. Compared with word-based methods, lattice LSTM does not suffer from segmentation errors. Gated recurrent cells allow our model to choose the most relevant characters and words from a sentence for better NER results. Experiments on various datasets show that lattice LSTM outperforms both word-based and character-based LSTM baselines, achieving the best results.

Connected through hyperedge HE-1 (induced by source paper "self attention based bigru and capsule network for named entity recognition"):
  with members:
    - "can ner convolutional attention network for chinese named entity recognition"
    - "investigating capsule networks with dynamic routing for text classification"
    - "a survey on deep learning for named entity recognition"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "dynamic routing between capsules"

Connected through hyperedge HE-2 (induced by source paper "fgn fusion glyph network for chinese named entity recognition"):
  with members:
    - "glyce glyph vectors for chinese character representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "memory fusion network for multi view sequential learning"
    - "tensor fusion network for multimodal sentiment analysis"
    - "character based joint segmentation and pos tagging for chinese using bidirectional rnn crf"
    - "attention is all you need"
    - "glyph aware embedding of chinese characters"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-3 (induced by source paper "integrating boundary assembling into a dnn framework for named entity recognition in chinese social media text"):
  with members:
    - "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"
    - "neural word segmentation learning for chinese"
    - "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-4 (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"
    - "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"
    - "bag of words as target for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "dice loss for data imbalanced nlp tasks"):
  with members:
    - "a unified mrc framework for named entity recognition"
    - "quoref a reading comprehension dataset with questions requiring coreferential reasoning"
    - "libra r cnn towards balanced learning for object detection"
    - "glyce glyph vectors for chinese character representations"
    - "towards accurate one stage object detection with ap loss"
    - "dsreg using distant supervision as a regularizer"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "the natural language decathlon multitask learning as question answering"

Connected through hyperedge HE-6 (induced by source paper "a unified mrc framework for named entity recognition"):
  with members:
    - "entity relation extraction as multi turn question answering"
    - "glyce glyph vectors for chinese character representations"
    - "nested named entity recognition via second best sequence learning and decoding"
    - "sequence to nuggets nested entity mention detection via anchor region networks"
    - "merge and label a novel neural network architecture for nested ner"
    - "neural architectures for nested ner through linearization"
    - "a general framework for information extraction using dynamic span graphs"
    - "the natural language decathlon multitask learning as question answering"

Connected through hyperedge HE-7 (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"
    - "subword encoding in lattice lstm for chinese word segmentation"
    - "finding better subword segmentation for neural machine translation"
    - "neural based pinyin to character conversion with adaptive vocabulary"

Connected through hyperedge HE-8 (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"
    - "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"
    - "attention is all you need"

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)