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: "a gru gated attention model for neural machine translation"
  Abstract: Neural machine translation (NMT) heavily relies on an attention network to produce a context vector for each target word prediction. In practice, we find that context vectors for different target words are quite similar to one another and therefore are insufficient in discriminatively predicting target words. The reason for this might be that context vectors produced by the vanilla attention network are just a weighted sum of source representations that are invariant to decoder states. In this paper, we propose a novel GRU-gated attention model (GAtt) for NMT which enhances the degree of discrimination of context vectors by enabling source representations to be sensitive to the partial translation generated by the decoder. GAtt uses a gated recurrent unit (GRU) to combine two types of information: treating a source annotation vector originally produced by the bidirectional encoder as the history state while the corresponding previous decoder state as the input to the GRU. The GRU-combined information forms a new source annotation vector. In this way, we can obtain translation-sensitive source representations which are then feed into the attention network to generate discriminative context vectors. We further propose a variant that regards a source annotation vector as the current input while the previous decoder state as the history. Experiments on NIST Chinese-English translation tasks show that both GAtt-based models achieve significant improvements over the vanilla attentionbased NMT. Further analyses on attention weights and context vectors demonstrate the effectiveness of GAtt in improving the discrimination power of representations and handling the challenging issue of over-translation.

Connected through hyperedge HE-1 (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-2 (induced by source paper "simplifying neural machine translation with addition subtraction twin gated recurrent networks"):
  with members:
    - "asynchronous bidirectional decoding for neural machine translation"
    - "variational recurrent neural machine translation"
    - "accelerating neural transformer via an average attention network"
    - "recurrent additive networks"
    - "attention is all you need"
    - "modeling past and future for neural machine translation"
    - "convolutional sequence to sequence learning"
    - "deep neural machine translation with linear associative unit"

Connected through hyperedge HE-3 (induced by source paper "variational recurrent neural machine translation"):
  with members:
    - "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"
    - "neural machine translation with recurrent attention modeling"

Connected through hyperedge HE-4 (induced by source paper "learning to refine source representations for neural machine translation"):
  with members:
    - "asynchronous bidirectional decoding for neural machine translation"
    - "learning to jointly translate and predict dropped pronouns with a shared reconstruction mechanism"
    - "attention is all you need"
    - "adversarial learning for neural dialogue generation"
    - "convolutional sequence to sequence learning"
    - "reading wikipedia to answer open domain questions"
    - "context gates for neural machine translation"
    - "neural machine translation with reconstruction"

Connected through hyperedge HE-5 (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"
    - "convolutional sequence to sequence learning"
    - "modeling coverage for neural machine translation"
    - "neural machine translation with recurrent attention modeling"
    - "incorporating structural alignment biases into an attentional neural translation model"

Connected through hyperedge HE-6 (induced by source paper "accelerating neural transformer via an average attention network"):
  with members:
    - "attention is all you need"
    - "non autoregressive neural machine translation"
    - "recurrent neural machine translation"
    - "neural machine translation with recurrent attention modeling"
    - "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.CL(Computation and Language)