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: "lattice based recurrent neural network encoders for neural machine translation"
  Abstract: Neural machine translation (NMT) heavily relies on word-level modelling to learn semantic representations of input sentences. However, for languages without natural word delimiters (e.g., Chinese) where input sentences have to be tokenized first, conventional NMT is confronted with two issues: 1) it is difficult to find an optimal tokenization granularity for source sentence modelling, and 2) errors in 1-best tokenizations may propagate to the encoder of NMT. To handle these issues, we propose word-lattice based Recurrent Neural Network (RNN) encoders for NMT, which generalize the standard RNN to word lattice topology. The proposed encoders take as input a word lattice that compactly encodes multiple tokenizations, and learn to generate new hidden states from arbitrarily many inputs and hidden states in preceding time steps. As such, the word-lattice based encoders not only alleviate the negative impact of tokenization errors but also are more expressive and flexible to embed input sentences. Experiment results on Chinese-English translation demonstrate the superiorities of the proposed encoders over the conventional encoder.

Connected through hyperedge HE-1 (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-2 (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"
    - "subword encoding in lattice lstm for chinese word segmentation"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "confusion2vec towards enriching vector space word representations with representational ambiguities"):
  with members:
    - "morphological word embeddings"
    - "learning from past mistakes improving automatic speech recognition output via noisy clean phrase context modeling"
    - "neural lattice language models"
    - "neural lattice to sequence models for uncertain inputs"
    - "enriching word vectors with subword information"
    - "multi view recurrent neural acoustic word embeddings"
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-4 (induced by source paper "lattice transformer for speech translation"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "unsupervised multi modal neural machine translation"
    - "chinese ner using lattice lstm"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "bilingual expert can find translation errors"
    - "self attention with relative position representations"
    - "neural lattice language models"
    - "subword regularization improving neural network translation models with multiple subword candidates"

Connected through hyperedge HE-5 (induced by source paper "modeling local dependence in natural language with multi channel recurrent neural networks"):
  with members:
    - "structured attention networks"
    - "on the state of the art of evaluation in neural language models"
    - "attention is all you need"
    - "frustratingly short attention spans in neural language modeling"
    - "dilated recurrent neural networks"
    - "regularizing and optimizing lstm language models"
    - "jointly learning sentence embeddings and syntax with unsupervised tree lstms"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-6 (induced by source paper "chinese ner using lattice lstm"):
  with members:
    - "cross sentence n ary relation extraction with graph lstms"
    - "combining discrete and neural features for sequence labeling"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "semi supervised multitask learning for sequence labeling"
    - "semi supervised sequence tagging with bidirectional language models"
    - "lattice long short term memory for human action recognition"
    - "yedda a lightweight collaborative text span annotation tool"

Connected through hyperedge HE-7 (induced by source paper "deconvolution based global decoding for neural machine translation"):
  with members:
    - "global encoding for abstractive summarization"
    - "decoding history based adaptive control of attention for neural machine translation"
    - "label embedding network learning label representation for soft training of deep networks"
    - "bag of words as target for neural machine translation"
    - "attention is all you need"
    - "deconvolutional latent variable model for text sequence matching"
    - "get to the point summarization with pointer generator networks"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-8 (induced by source paper "subword regularization improving neural network translation models with multiple subword candidates"):
  with members:
    - "unsupervised machine translation using monolingual corpora only"
    - "attention is all you need"
    - "stronger baselines for trustable results in neural machine translation"
    - "data noising as smoothing in neural network language models"
    - "synthetic and natural noise both break neural machine translation"
    - "convolutional sequence to sequence learning"
    - "neural lattice to sequence models for uncertain inputs"
    - "google s neural machine translation system bridging the gap between human and machine translation"

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)