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 convolutional encoder model for neural machine translation"
  Abstract: The prevalent approach to neural machine translation relies on bi-directional LSTMs to encode the source sentence. In this paper we present a faster and simpler architecture based on a succession of convolutional layers. This allows to encode the entire source sentence simultaneously compared to recurrent networks for which computation is constrained by temporal dependencies. On WMT'16 English-Romanian translation we achieve competitive accuracy to the state-of-the-art and we outperform several recently published results on the WMT'15 English-German task. Our models obtain almost the same accuracy as a very deep LSTM setup on WMT'14 English-French translation. Our convolutional encoder speeds up CPU decoding by more than two times at the same or higher accuracy as a strong bi-directional LSTM baseline.

Connected through hyperedge HE-1 (induced by source paper "gret global representation enhanced transformer"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "context aware self attention networks"
    - "dynamic past and future for neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "deconvolution based global decoding for neural machine translation"
    - "exploiting deep representations for neural machine translation"
    - "global encoding for abstractive summarization"
    - "investigating capsule networks with dynamic routing for text classification"

Connected through hyperedge HE-2 (induced by source paper "multi layer representation fusion for neural machine translation"):
  with members:
    - "path aggregation network for instance segmentation"
    - "towards neural phrase based machine translation"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "socialgrid a tcn enhanced method for online discussion forecasting"):
  with members:
    - "nestpp modeling thread dynamics in online discussion forums"
    - "modelling structure and predicting dynamics of discussion threads in online boards"
    - "personalized thread recommendation for mooc discussion forums"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "multi scale convolutional neural networks for time series classification"
    - "tideh time dependent hawkes process for predicting retweet dynamics"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-4 (induced by source paper "blk rew a unified block based dnn pruning framework using reweighted regularization method"):
  with members:
    - "2pfpce two phase filter pruning based on conditional entropy"
    - "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers"
    - "amc automl for model compression and acceleration on mobile devices"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "discrimination aware channel pruning for deep neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "adam admm a unified systematic framework of structured weight pruning for dnns"
    - "e rnn design optimization for efficient recurrent neural networks in fpgas"

Connected through hyperedge HE-5 (induced by source paper "investigating the decoders of maximum likelihood sequence models a look ahead approach"):
  with members:
    - "on nmt search errors and model errors cat got your tongue"
    - "end to end dense video captioning with masked transformer"
    - "a call for clarity in reporting bleu scores"
    - "tvqa localized compositional video question answering"
    - "optimal completion distillation for sequence learning"
    - "attention is all you need"
    - "adversarial learning for neural dialogue generation"
    - "towards better decoding and language model integration in sequence to sequence models"

Connected through hyperedge HE-6 (induced by source paper "latent opinions transfer network for target oriented opinion words extraction"):
  with members:
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "attention is all you need"
    - "recursive neural conditional random fields for aspect based sentiment analysis"
    - "effective lstms for target dependent sentiment classification"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-7 (induced by source paper "what is this article about extreme summarization with topic aware convolutional neural networks"):
  with members:
    - "fine tune bert for extractive summarization"
    - "jointly extracting and compressing documents with summary state representations"
    - "scisummnet a large annotated corpus and content impact models for scientific paper summarization with citation networks"
    - "mass masked sequence to sequence pre training for language generation"
    - "generating summaries with topic templates and structured convolutional decoders"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "unified language model pre training for natural language understanding and generation"
    - "deep communicating agents for abstractive summarization"

Connected through hyperedge HE-8 (induced by source paper "the compositionality of neural networks integrating symbolism and connectionism"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "cnns found to jump around more skillfully than rnns compositional generalization in seq2seq convolutional networks"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "measuring compositionality in representation learning"
    - "analyzing the structure of attention in a transformer language model"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "the emergence of number and syntax units in lstm language models"

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)