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: "achieving human parity on automatic chinese to english news translation"
  Abstract: Machine translation has made rapid advances in recent years. Millions of people are using it today in online translation systems and mobile applications in order to communicate across language barriers. The question naturally arises whether such systems can approach or achieve parity with human translations. In this paper, we first address the problem of how to define and accurately measure human parity in translation. We then describe Microsoft's machine translation system and measure the quality of its translations on the widely used WMT 2017 news translation task from Chinese to English. We find that our latest neural machine translation system has reached a new state-of-the-art, and that the translation quality is at human parity when compared to professional human translations. We also find that it significantly exceeds the quality of crowd-sourced non-professional translations.

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"
    - "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"
    - "self attention with relative position representations"

Connected through hyperedge HE-2 (induced by source paper "capturing document context inside sentence level neural machine translation models with self training"):
  with members:
    - "putting machine translation in context with the noisy channel model"
    - "microsoft translator at wmt 2019 towards large scale document level neural machine translation"
    - "context aware monolingual repair for neural machine translation"
    - "when a good translation is wrong in context context aware machine translation improves on deixis ellipsis and lexical cohesion"
    - "revisiting self training for neural sequence generation"
    - "fill in the blanks imputing missing sentences for larger context neural machine translation"
    - "modeling coherence for discourse neural machine translation"
    - "improving the transformer translation model with document level context"

Connected through hyperedge HE-3 (induced by source paper "marathi to english neural machine translation with near perfect corpus and transformers"):
  with members:
    - "massively multilingual neural machine translation in the wild findings and challenges"
    - "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 "accelerating deep learning inference via freezing"):
  with members:
    - "skipnet learning dynamic routing in convolutional networks"
    - "deep learning with low precision by half wave gaussian quantization"
    - "clipper a low latency online prediction serving system"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "large scale pretraining for neural machine translation with tens of billions of sentence pairs"):
  with members:
    - "facebook fair s wmt19 news translation task submission"
    - "mass masked sequence to sequence pre training for language generation"
    - "mixture models for diverse machine translation tricks of the trade"
    - "is word segmentation necessary for deep learning of chinese representations"
    - "cross lingual language model pretraining"
    - "massively multilingual neural machine translation"
    - "hard but robust easy but sensitive how encoder and decoder perform in neural machine translation"
    - "a tutorial on deep latent variable models of natural language"

Connected through hyperedge HE-6 (induced by source paper "structure invariant testing for machine translation"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "machine learning testing survey landscapes and horizons"
    - "defensive quantization when efficiency meets robustness"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "sirenattack generating adversarial audio for end to end acoustic systems"
    - "code2vec learning distributed representations of code"
    - "on adversarial examples for character level neural machine translation"
    - "adversarial example generation with syntactically controlled paraphrase networks"

Connected through hyperedge HE-7 (induced by source paper "mass masked sequence to sequence pre training for language generation"):
  with members:
    - "unsupervised pivot translation for distant languages"
    - "multilingual neural machine translation with knowledge distillation"
    - "cross lingual language model pretraining"
    - "unsupervised statistical machine translation"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "double path networks for sequence to sequence learning"
    - "an efficient framework for learning sentence representations"

Connected through hyperedge HE-8 (induced by source paper "context aware self attention networks"):
  with members:
    - "neural machine translation with adequacy oriented learning"
    - "exploiting deep representations for neural machine translation"
    - "tied multitask learning for neural speech translation"
    - "learning to jointly translate and predict dropped pronouns with a shared reconstruction mechanism"
    - "self attention with relative position representations"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "modeling localness for self attention networks"
    - "deep contextualized word representations"

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)