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 word predictions"
  Abstract: In the encoder-decoder architecture for neural machine translation (NMT), the hidden states of the recurrent structures in the encoder and decoder carry the crucial information about the sentence.These vectors are generated by parameters which are updated by back-propagation of translation errors through time. We argue that propagating errors through the end-to-end recurrent structures are not a direct way of control the hidden vectors. In this paper, we propose to use word predictions as a mechanism for direct supervision. More specifically, we require these vectors to be able to predict the vocabulary in target sentence. Our simple mechanism ensures better representations in the encoder and decoder without using any extra data or annotation. It is also helpful in reducing the target side vocabulary and improving the decoding efficiency. Experiments on Chinese-English and German-English machine translation tasks show BLEU improvements by 4.53 and 1.3, respectively

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 "modeling future cost for neural machine translation"):
  with members:
    - "dynamic past and future for neural machine translation"
    - "attention is all you need"
    - "coverage embedding models for neural machine translation"
    - "modeling coverage for neural machine translation"
    - "deep residual learning for image recognition"
    - "neural machine translation of rare words with subword units"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "dynamic past and future for neural machine translation"):
  with members:
    - "synchronous bidirectional neural machine translation"
    - "dynamic layer aggregation for neural machine translation with routing by agreement"
    - "information aggregation for multi head attention with routing by agreement"
    - "neural machine translation with adequacy oriented learning"
    - "deconvolution based global decoding for neural machine translation"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "investigating capsule networks with dynamic routing for text classification"
    - "information aggregation via dynamic routing for sequence encoding"

Connected through hyperedge HE-4 (induced by source paper "generating sentences from disentangled syntactic and semantic spaces"):
  with members:
    - "disentangled representation learning for non parallel text style transfer"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "bag of words as target for neural machine translation"
    - "cgmh constrained sentence generation by metropolis hastings sampling"
    - "syntax directed variational autoencoder for structured data"
    - "toward controlled generation of text"
    - "a hybrid convolutional variational autoencoder for text generation"

Connected through hyperedge HE-5 (induced by source paper "improved variational neural machine translation by promoting mutual information"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "preventing posterior collapse with delta vaes"
    - "mixture models for diverse machine translation tricks of the trade"
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "variational recurrent neural machine translation"
    - "scaling neural machine translation"
    - "bag of words as target for neural machine translation"
    - "a call for clarity in reporting bleu scores"

Connected through hyperedge HE-6 (induced by source paper "learning to reuse translations guiding neural machine translation with examples"):
  with members:
    - "exploiting deep representations for neural machine translation"
    - "skeleton to response dialogue generation guided by retrieval memory"
    - "tied multitask learning for neural speech translation"
    - "learning to jointly translate and predict dropped pronouns with a shared reconstruction mechanism"
    - "improving the transformer translation model with document level context"
    - "parameter sharing methods for multilingual self attentional translation models"
    - "fusing recency into neural machine translation with an inter sentence gate model"
    - "deep contextualized word representations"

Connected through hyperedge HE-7 (induced by source paper "hscjn a holistic semantic constraint joint network for diverse response generation"):
  with members:
    - "entropy enhanced multimodal attention model for scene aware dialogue generation"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "dp gan diversity promoting generative adversarial network for generating informative and diversified text"
    - "a knowledge grounded neural conversation model"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "vocabulary selection strategies for neural machine translation"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "a simple fast diverse decoding algorithm for neural generation"

Connected through hyperedge HE-8 (induced by source paper "learning to discriminate noises for incorporating external information in neural machine translation"):
  with members:
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "context aware neural machine translation learns anaphora resolution"
    - "neural machine translation decoding with terminology constraints"
    - "translating phrases in neural machine translation"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "neural system combination for machine translation"
    - "learning to remember translation history with a continuous cache"
    - "attention is all you need"

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)