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: "regularizing neural machine translation by target bidirectional agreement"
  Abstract: Although Neural Machine Translation (NMT) has achieved remarkable progress in the past several years, most NMT systems still suffer from a fundamental shortcoming as in other sequence generation tasks: errors made early in generation process are fed as inputs to the model and can be quickly amplified, harming subsequent sequence generation. To address this issue, we propose a novel model regularization method for NMT training, which aims to improve the agreement between translations generated by left-to-right (L2R) and right-to-left (R2L) NMT decoders. This goal is achieved by introducing two Kullback-Leibler divergence regularization terms into the NMT training objective to reduce the mismatch between output probabilities of L2R and R2L models. In addition, we also employ a joint training strategy to allow L2R and R2L models to improve each other in an interactive update process. Experimental results show that our proposed method significantly outperforms state-of-the-art baselines on Chinese-English and English-German translation tasks.

Connected through hyperedge HE-1 (induced by source paper "distilling the knowledge of bert for text generation"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "towards making the most of bert in neural machine translation"
    - "mask predict parallel decoding of conditional masked language models"
    - "multilingual neural machine translation with knowledge distillation"
    - "mass masked sequence to sequence pre training for language generation"
    - "pretraining based natural language generation for text summarization"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "patient knowledge distillation for bert model compression"

Connected through hyperedge HE-2 (induced by source paper "regressing word and sentence embeddings for regularization of neural machine translation"):
  with members:
    - "rewe regressing word embeddings for regularization of neural machine translation systems"
    - "pre trained language model representations for language generation"
    - "differentiable sampling with flexible reference word order for neural machine translation"
    - "training neural machine translation using word embedding based loss"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "von mises fisher loss for training sequence to sequence models with continuous outputs"
    - "deep contextualized word representations"
    - "universal sentence encoder"

Connected through hyperedge HE-3 (induced by source paper "improving multi head attention with capsule networks"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "information aggregation for multi head attention with routing by agreement"
    - "weighted transformer network for machine translation"
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "investigating capsule networks with dynamic routing for text classification"
    - "self attention with relative position representations"
    - "dtmt a novel deep transition architecture for neural machine translation"
    - "scaling neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "rewe regressing word embeddings for regularization of neural machine translation systems"):
  with members:
    - "training neural machine translation using word embedding based loss"
    - "token level and sequence level loss smoothing for rnn language models"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "bag of words as target for neural machine translation"
    - "phrase based neural unsupervised machine translation"
    - "data augmentation for low resource neural machine translation"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "synchronous bidirectional inference for neural sequence generation"):
  with members:
    - "synchronous bidirectional neural machine translation"
    - "pay less attention with lightweight and dynamic convolutions"
    - "bridging the gap between training and inference for neural machine translation"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "self attention with relative position representations"
    - "pervasive attention 2d convolutional neural networks for sequence to sequence prediction"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "selective encoding for abstractive sentence summarization"

Connected through hyperedge HE-6 (induced by source paper "improving bidirectional decoding with dynamic target semantics in neural machine translation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "synchronous bidirectional inference for neural sequence generation"
    - "achieving human parity on automatic chinese to english news translation"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "dtmt a novel deep transition architecture for neural machine translation"
    - "neural machine translation with key value memory augmented attention"
    - "the best of both worlds combining recent advances in neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "unsupervised neural machine translation with smt as posterior regularization"):
  with members:
    - "on the impact of various types of noise on neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "prior knowledge integration for neural machine translation using posterior regularization"
    - "joint training for neural machine translation models with monolingual data"
    - "phrase based neural unsupervised machine translation"
    - "unsupervised neural machine translation with weight sharing"
    - "unsupervised machine translation using monolingual corpora only"
    - "attention is all you need"

Connected through hyperedge HE-8 (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"
    - "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"
    - "qanet combining local convolution with global self attention for reading comprehension"

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)