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: "massively multilingual neural machine translation in the wild findings and challenges"
  Abstract: We introduce our efforts towards building a universal neural machine translation (NMT) system capable of translating between any language pair. We set a milestone towards this goal by building a single massively multilingual NMT model handling 103 languages trained on over 25 billion examples. Our system demonstrates effective transfer learning ability, significantly improving translation quality of low-resource languages, while keeping high-resource language translation quality on-par with competitive bilingual baselines. We provide in-depth analysis of various aspects of model building that are crucial to achieving quality and practicality in universal NMT. While we prototype a high-quality universal translation system, our extensive empirical analysis exposes issues that need to be further addressed, and we suggest directions for future research.

Connected through hyperedge HE-1 (induced by source paper "multilingual denoising pre training for neural machine translation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "unsupervised cross lingual representation learning at scale"
    - "improved zero shot neural machine translation via ignoring spurious correlations"
    - "pre trained language model representations for language generation"
    - "ctrl a conditional transformer language model for controllable generation"
    - "mass masked sequence to sequence pre training for language generation"
    - "pretrained language models for document level neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "a multilingual view of unsupervised machine translation"):
  with members:
    - "a probabilistic formulation of unsupervised text style transfer"
    - "multilingual denoising pre training for neural machine translation"
    - "improved zero shot neural machine translation via ignoring spurious correlations"
    - "explicit cross lingual pre training for unsupervised machine translation"
    - "an effective approach to unsupervised machine translation"
    - "mass masked sequence to sequence pre training for language generation"
    - "consistency by agreement in zero shot neural machine translation"
    - "cross lingual language model pretraining"

Connected through hyperedge HE-3 (induced by source paper "marathi to english neural machine translation with near perfect corpus and transformers"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "training deeper neural machine translation models with transparent attention"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "deep recurrent models with fast forward connections for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "unsupervised cross lingual representation learning at scale"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "unicoder a universal language encoder by pre training with multiple cross lingual tasks"
    - "evaluating the cross lingual effectiveness of massively multilingual neural machine translation"
    - "multilingual neural machine translation with knowledge distillation"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "unsupervised data augmentation for consistency training"
    - "ccnet extracting high quality monolingual datasets from web crawl data"
    - "mlqa evaluating cross lingual extractive question answering"

Connected through hyperedge HE-5 (induced by source paper "can monolingual pretrained models help cross lingual classification"):
  with members:
    - "adversarial learning with contextual embeddings for zero resource cross lingual classification and ner"
    - "unsupervised cross lingual representation learning at scale"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "multifit efficient multi lingual language model fine tuning"
    - "how multilingual is multilingual bert"
    - "cross lingual language model pretraining"
    - "cross lingual natural language generation via pre training"
    - "xnli evaluating cross lingual sentence representations"

Connected through hyperedge HE-6 (induced by source paper "gpipe efficient training of giant neural networks using pipeline parallelism"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "fixup initialization residual learning without normalization"
    - "object part attention model for fine grained image classification"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "do better imagenet models transfer better"
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-7 (induced by source paper "efficient and robust parallel dnn training through model parallelism on multi gpu platform"):
  with members:
    - "beyond data and model parallelism for deep neural networks"
    - "regularized evolution for image classifier architecture search"
    - "mxnet mpi embedding mpi parallelism in parameter server task model for scaling deep learning"
    - "training neural networks using features replay"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "pipedream fast and efficient pipeline parallel dnn training"
    - "exploring hidden dimensions in parallelizing convolutional neural networks"
    - "self normalizing neural networks"

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)