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: "towards neural phrase based machine translation"
  Abstract: In this paper, we present Neural Phrase-based Machine Translation (NPMT). Our method explicitly models the phrase structures in output sequences using Sleep-WAke Networks (SWAN), a recently proposed segmentation-based sequence modeling method. To mitigate the monotonic alignment requirement of SWAN, we introduce a new layer to perform (soft) local reordering of input sequences. Different from existing neural machine translation (NMT) approaches, NPMT does not use attention-based decoding mechanisms. Instead, it directly outputs phrases in a sequential order and can decode in linear time. Our experiments show that NPMT achieves superior performances on IWSLT 2014 German-English/English-German and IWSLT 2015 English-Vietnamese machine translation tasks compared with strong NMT baselines. We also observe that our method produces meaningful phrases in output languages.

Connected through hyperedge HE-1 (induced by source paper "multi layer representation fusion for neural machine translation"):
  with members:
    - "path aggregation network for instance segmentation"
    - "attention is all you need"
    - "stronger baselines for trustable results in neural machine translation"
    - "classical structured prediction losses for sequence to sequence learning"
    - "multi channel encoder for neural machine translation"
    - "convolutional sequence to sequence learning"
    - "deep neural machine translation with linear associative unit"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-2 (induced by source paper "explicit sparse transformer concentrated attention through explicit selection"):
  with members:
    - "adaptively sparse transformers"
    - "muse parallel multi scale attention for sequence to sequence learning"
    - "pay less attention with lightweight and dynamic convolutions"
    - "sparse sequence to sequence models"
    - "joint source target self attention with locality constraints"
    - "transformer xl attentive language models beyond a fixed length context"
    - "fixup initialization residual learning without normalization"
    - "universal transformers"

Connected through hyperedge HE-3 (induced by source paper "muse parallel multi scale attention for sequence to sequence learning"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "augmenting self attention with persistent memory"
    - "pay less attention with lightweight and dynamic convolutions"
    - "joint source target self attention with locality constraints"
    - "the evolved transformer"
    - "transformer xl attentive language models beyond a fixed length context"
    - "depth growing for neural machine translation"
    - "fixup initialization residual learning without normalization"

Connected through hyperedge HE-4 (induced by source paper "efficient bidirectional neural machine translation"):
  with members:
    - "mass masked sequence to sequence pre training for language generation"
    - "achieving human parity on automatic chinese to english news translation"
    - "beyond error propagation in neural machine translation characteristics of language also matter"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "dense information flow for neural machine translation"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "hint based training for non autoregressive machine translation"):
  with members:
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "attention guided answer distillation for machine reading comprehension"
    - "fast decoding in sequence models using discrete latent variables"
    - "attention is all you need"
    - "non autoregressive neural machine translation"
    - "classical structured prediction losses for sequence to sequence learning"
    - "convolutional sequence to sequence learning"
    - "sequence level knowledge distillation"

Connected through hyperedge HE-6 (induced by source paper "pervasive attention 2d convolutional neural networks for sequence to sequence prediction"):
  with members:
    - "towards two dimensional sequence to sequence model in neural machine translation"
    - "weaver deep co encoding of questions and documents for machine reading"
    - "adversarial neural machine translation"
    - "attention is all you need"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "classical structured prediction losses for sequence to sequence learning"
    - "convolutional sequence to sequence learning"
    - "a decomposable attention model for natural language inference"

Connected through hyperedge HE-7 (induced by source paper "towards binary valued gates for robust lstm training"):
  with members:
    - "learning to generate long term future via hierarchical prediction"
    - "on the state of the art of evaluation in neural language models"
    - "automatic rule extraction from long short term memory networks"
    - "regularizing and optimizing lstm language models"
    - "massive exploration of neural machine translation architectures"
    - "convolutional sequence to sequence learning"
    - "sequence to sequence learning as beam search optimization"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Connected through hyperedge HE-8 (induced by source paper "frage frequency agnostic word representation"):
  with members:
    - "fix your classifier the marginal value of training the last weight layer"
    - "analyzing uncertainty in neural machine translation"
    - "unsupervised machine translation using monolingual corpora only"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "attention is all you need"
    - "adversarial discriminative domain adaptation"
    - "regularizing and optimizing lstm language models"
    - "classical structured prediction losses for 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)