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: "asynchronous bidirectional decoding for neural machine translation"
  Abstract: The dominant neural machine translation (NMT) models apply unified attentional encoder-decoder neural networks for translation. Traditionally, the NMT decoders adopt recurrent neural networks (RNNs) to perform translation in a left-toright manner, leaving the target-side contexts generated from right to left unexploited during translation. In this paper, we equip the conventional attentional encoder-decoder NMT framework with a backward decoder, in order to explore bidirectional decoding for NMT. Attending to the hidden state sequence produced by the encoder, our backward decoder first learns to generate the target-side hidden state sequence from right to left. Then, the forward decoder performs translation in the forward direction, while in each translation prediction timestep, it simultaneously applies two attention models to consider the source-side and reverse target-side hidden states, respectively. With this new architecture, our model is able to fully exploit source- and target-side contexts to improve translation quality altogether. Experimental results on NIST Chinese-English and WMT English-German translation tasks demonstrate that our model achieves substantial improvements over the conventional NMT by 3.14 and 1.38 BLEU points, respectively. The source code of this work can be obtained from this https URL

Connected through hyperedge HE-1 (induced by source paper "pseudo bidirectional decoding for local sequence transduction"):
  with members:
    - "improving grammatical error correction with machine translation pairs"
    - "an empirical study of incorporating pseudo data into grammatical error correction"
    - "sequence to sequence pre training with data augmentation for sentence rewriting"
    - "albert a lite bert for self supervised learning of language representations"
    - "synchronous bidirectional inference for neural sequence generation"
    - "parallel iterative edit models for local sequence transduction"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "icap interative image captioning with predictive text"):
  with members:
    - "fast interactive object annotation with curve gcn"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "show control and tell a framework for generating controllable and grounded captions"
    - "annotating object instances with a polygon rnn"
    - "twin networks matching the future for sequence generation"
    - "the imagenet shuffle reorganized pre training for video event detection"
    - "self critical sequence training for image captioning"
    - "show and tell a neural image caption generator"

Connected through hyperedge HE-3 (induced by source paper "dynamic past and future for neural machine translation"):
  with members:
    - "synchronous bidirectional neural machine translation"
    - "dynamic layer aggregation for neural machine translation with routing by agreement"
    - "information aggregation for multi head attention with routing by agreement"
    - "neural machine translation with adequacy oriented learning"
    - "deconvolution based global decoding for neural machine translation"
    - "investigating capsule networks with dynamic routing for text classification"
    - "information aggregation via dynamic routing for sequence encoding"
    - "a hybrid word character model for abstractive summarization"

Connected through hyperedge HE-4 (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"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "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-5 (induced by source paper "improving deep transformer with depth scaled initialization and merged attention"):
  with members:
    - "very deep self attention networks for end to end speech recognition"
    - "pay less attention with lightweight and dynamic convolutions"
    - "the evolved transformer"
    - "generating long sequences with sparse transformers"
    - "blockwise parallel decoding for deep autoregressive models"
    - "scaling neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a call for clarity in reporting bleu scores"

Connected through hyperedge HE-6 (induced by source paper "bidirectional scene text recognition with a single decoder"):
  with members:
    - "synchronous bidirectional neural machine translation"
    - "what is wrong with scene text recognition model comparisons dataset and model analysis"
    - "symmetry constrained rectification network for scene text recognition"
    - "nrtr a no recurrence sequence to sequence model for scene text recognition"
    - "esir end to end scene text recognition via iterative image rectification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "attention is all you need"

Connected through hyperedge HE-7 (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"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "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-8 (induced by source paper "synchronous bidirectional neural machine translation"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "breaking the beam search curse a study of re scoring methods and stopping criteria for neural machine translation"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "neural system combination for machine translation"
    - "attention is all you need"
    - "modeling past and future for neural machine translation"
    - "learning to decode for future success"
    - "convolutional sequence to sequence learning"

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)