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: "pervasive attention 2d convolutional neural networks for sequence to sequence prediction"
  Abstract: Current state-of-the-art machine translation systems are based on encoder-decoder architectures, that first encode the input sequence, and then generate an output sequence based on the input encoding. Both are interfaced with an attention mechanism that recombines a fixed encoding of the source tokens based on the decoder state. We propose an alternative approach which instead relies on a single 2D convolutional neural network across both sequences. Each layer of our network re-codes source tokens on the basis of the output sequence produced so far. Attention-like properties are therefore pervasive throughout the network. Our model yields excellent results, outperforming state-of-the-art encoder-decoder systems, while being conceptually simpler and having fewer parameters.

Connected through hyperedge HE-1 (induced by source paper "neural machine translation with joint representation"):
  with members:
    - "learning deep transformer models for machine translation"
    - "towards two dimensional sequence to sequence model in neural machine translation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "understanding deep learning requires rethinking generalization"
    - "deep residual learning for image recognition"
    - "neural machine translation of rare words with subword units"
    - "grid long short term memory"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "convolution attention and structure embedding"):
  with members:
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "attention is all you need"
    - "position based content attention for time series forecasting with sequence to sequence rnns"
    - "deep biaffine attention for neural dependency parsing"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "xception deep learning with depthwise separable convolutions"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

Connected through hyperedge HE-3 (induced by source paper "revisiting low resource neural machine translation a case study"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "revisiting character based neural machine translation with capacity and compression"
    - "unsupervised statistical machine translation"
    - "trivial transfer learning for low resource neural machine translation"
    - "universal neural machine translation for extremely low resource languages"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "a call for clarity in reporting bleu scores"
    - "rapid adaptation of neural machine translation to new languages"

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"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "self attention with relative position representations"
    - "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 "sequence modeling with unconstrained generation order"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "non monotonic sequential text generation"
    - "universal transformers"
    - "middle out decoding"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "beyond error propagation in neural machine translation characteristics of language also matter"
    - "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 "joint source target self attention with locality constraints"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "cross lingual language model pretraining"
    - "weighted transformer network for machine translation"
    - "self attention with relative position representations"
    - "scaling neural machine translation"
    - "modeling localness for self attention networks"
    - "a call for clarity in reporting bleu scores"
    - "training deeper neural machine translation models with transparent attention"

Connected through hyperedge HE-7 (induced by source paper "empirical evaluation of sequence to sequence models for word discovery in low resource settings"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "adding interpretable attention to neural translation models improves word alignment"
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "hybrid self attention network for machine translation"
    - "multi head attention with disagreement regularization"
    - "a case study on using speech to translation alignments for language documentation"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "regularized context gates on transformer for machine translation"):
  with members:
    - "towards robust neural machine translation"
    - "bag of words as target for neural machine translation"
    - "thumt an open source toolkit for neural machine translation"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "context gates for neural machine translation"
    - "neural machine translation with supervised attention"
    - "supervised attentions 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)