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 key value memory augmented attention"
  Abstract: Although attention-based Neural Machine Translation (NMT) has achieved remarkable progress in recent years, it still suffers from issues of repeating and dropping translations. To alleviate these issues, we propose a novel key-value memory-augmented attention model for NMT, called KVMEMATT. Specifically, we maintain a timely updated keymemory to keep track of attention history and a fixed value-memory to store the representation of source sentence throughout the whole translation process. Via nontrivial transformations and iterative interactions between the two memories, the decoder focuses on more appropriate source word(s) for predicting the next target word at each decoding step, therefore can improve the adequacy of translations. Experimental results on Chinese=>English and WMT17 German English translation tasks demonstrate the superiority of the proposed model.

Connected through hyperedge HE-1 (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"
    - "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"
    - "the best of both worlds combining recent advances in neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "learning efficient lexically constrained neural machine translation with external memory"):
  with members:
    - "towards one shot learning for rare word translation with external experts"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "a call for clarity in reporting bleu scores"
    - "phrase table as recommendation memory for neural machine translation"
    - "phrase based neural unsupervised machine translation"
    - "unsupervised machine translation using monolingual corpora only"
    - "attention is all you need"
    - "lexically constrained decoding for sequence generation using grid beam search"

Connected through hyperedge HE-3 (induced by source paper "dtmt a novel deep transition architecture for neural machine translation"):
  with members:
    - "towards robust neural machine translation"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "deep neural machine translation with linear associative unit"
    - "deep architectures for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "neural machine translation with adequacy oriented learning"):
  with members:
    - "towards robust neural machine translation"
    - "thumt an open source toolkit for neural machine translation"
    - "adversarial neural machine translation"
    - "attention is all you need"
    - "modeling past and future for neural machine translation"
    - "improving neural machine translation with conditional sequence generative adversarial nets"
    - "convolutional sequence to sequence learning"
    - "six challenges for neural 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)