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: "neural machine translation with recurrent attention modeling"
  Abstract: Knowing which words have been attended to in previous time steps while generating a translation is a rich source of information for predicting what words will be attended to in the future. We improve upon the attention model of Bahdanau et al. (2014) by explicitly modeling the relationship between previous and subsequent attention levels for each word using one recurrent network per input word. This architecture easily captures informative features, such as fertility and regularities in relative distortion. In experiments, we show our parameterization of attention improves translation quality.

Connected through hyperedge HE-1 (induced by source paper "sequential latent knowledge selection for knowledge grounded dialogue"):
  with members:
    - "long and diverse text generation with planning based hierarchical variational model"
    - "sequential latent spaces for modeling the intention during diverse image captioning"
    - "incremental transformer with deliberation decoder for document grounded conversations"
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "acute eval improved dialogue evaluation with optimized questions and multi turn comparisons"
    - "deepcopy grounded response generation with hierarchical pointer networks"
    - "visualizing and understanding the effectiveness of bert"

Connected through hyperedge HE-2 (induced by source paper "structure invariant testing for machine translation"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "machine learning testing survey landscapes and horizons"
    - "defensive quantization when efficiency meets robustness"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "sirenattack generating adversarial audio for end to end acoustic systems"
    - "achieving human parity on automatic chinese to english news translation"
    - "code2vec learning distributed representations of code"
    - "on adversarial examples for character level neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "controlling utterance length in nmt based word segmentation with attention"):
  with members:
    - "attention is not not explanation"
    - "attention is not explanation"
    - "tied multitask learning for neural speech translation"
    - "learning to segment inputs for nmt favors character level processing"
    - "low resource speech to text translation"
    - "on the alignment problem in multi head attention based neural machine translation"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "subword regularization improving neural network translation models with multiple subword candidates"

Connected through hyperedge HE-4 (induced by source paper "variational recurrent neural machine translation"):
  with members:
    - "a gru gated attention model for neural machine translation"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "modeling source syntax for neural machine translation"
    - "deep neural machine translation with linear associative unit"
    - "memory enhanced decoder for neural machine translation"
    - "variational neural machine translation"
    - "interactive attention for neural machine translation"
    - "modeling coverage for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "asynchronous bidirectional decoding for neural machine translation"):
  with members:
    - "an exploration of neural sequence to sequence architectures for automatic post editing"
    - "neural system combination for machine translation"
    - "deep neural machine translation with linear associative unit"
    - "towards decoding as continuous optimization in neural machine translation"
    - "twin networks matching the future for sequence generation"
    - "memory enhanced decoder for neural machine translation"
    - "modeling coverage for neural machine translation"
    - "edinburgh neural machine translation systems for wmt 16"

Connected through hyperedge HE-6 (induced by source paper "document level neural machine translation with hierarchical attention networks"):
  with members:
    - "translating pro drop languages with reconstruction models"
    - "context aware neural machine translation learns anaphora resolution"
    - "neural machine translation with extended context"
    - "learning to remember translation history with a continuous cache"
    - "attention is all you need"
    - "evaluating discourse phenomena in neural machine translation"
    - "opennmt open source toolkit for neural machine translation"
    - "exploiting cross sentence context for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "otem utem over and under translation evaluation metric for nmt"):
  with members:
    - "pairwise neural machine translation evaluation"
    - "sparse and constrained attention for neural machine translation"
    - "lepor an augmented machine translation evaluation metric"
    - "attention is all you need"
    - "a gru gated attention model for neural machine translation"
    - "convolutional sequence to sequence learning"
    - "modeling coverage for neural machine translation"
    - "incorporating structural alignment biases into an attentional neural translation model"

Connected through hyperedge HE-8 (induced by source paper "accelerating neural transformer via an average attention network"):
  with members:
    - "attention is all you need"
    - "non autoregressive neural machine translation"
    - "a gru gated attention model for neural machine translation"
    - "recurrent neural machine translation"
    - "supervised attentions for neural machine translation"
    - "a convolutional encoder model for neural machine translation"
    - "effective approaches to attention based neural machine translation"
    - "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.NE(Neural and Evolutionary Computing)