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: "training deeper neural machine translation models with transparent attention"
  Abstract: While current state-of-the-art NMT models, such as RNN seq2seq and Transformers, possess a large number of parameters, they are still shallow in comparison to convolutional models used for both text and vision applications. In this work we attempt to train significantly (2-3x) deeper Transformer and Bi-RNN encoders for machine translation. We propose a simple modification to the attention mechanism that eases the optimization of deeper models, and results in consistent gains of 0.7-1.1 BLEU on the benchmark WMT'14 English-German and WMT'15 Czech-English tasks for both architectures.

Connected through hyperedge HE-1 (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"
    - "achieving human parity on automatic chinese to english news translation"
    - "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-2 (induced by source paper "simple scalable adaptation for neural machine translation"):
  with members:
    - "parameter efficient transfer learning for nlp"
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "non parametric adaptation for neural machine translation"
    - "massively multilingual neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "freezing subnetworks to analyze domain adaptation in neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

Connected through hyperedge HE-3 (induced by source paper "character based nmt with transformer"):
  with members:
    - "naver labs europe s systems for the wmt19 machine translation robustness task"
    - "learning deep transformer models for machine translation"
    - "improving robustness of machine translation with synthetic noise"
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "revisiting character based neural machine translation with capacity and compression"
    - "learning to segment inputs for nmt favors character level processing"
    - "character level language modeling with deeper self attention"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-4 (induced by source paper "deep modular co attention networks for visual question answering"):
  with members:
    - "learning to count objects in natural images for visual question answering"
    - "rethinking diversified and discriminative proposal generation for visual grounding"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "attention is all you need"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"

Connected through hyperedge HE-5 (induced by source paper "improving deep transformer with depth scaled initialization and merged attention"):
  with members:
    - "very deep self attention networks for end to end speech recognition"
    - "pay less attention with lightweight and dynamic convolutions"
    - "the evolved transformer"
    - "generating long sequences with sparse transformers"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "blockwise parallel decoding for deep autoregressive models"
    - "scaling neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "learning deep transformer models for machine translation"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "exploiting deep representations for neural machine translation"
    - "self attention with relative position representations"
    - "scaling neural machine translation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character level language modeling with deeper self attention"
    - "the best of both worlds combining recent advances in neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "robust neural machine translation with doubly adversarial inputs"):
  with members:
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "towards robust neural machine translation"
    - "on adversarial examples for character level neural machine translation"
    - "generating natural language adversarial examples"
    - "robust neural machine translation with joint textual and phonetic embedding"
    - "mtnt a testbed for machine translation of noisy text"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "understanding back translation at scale"

Connected through hyperedge HE-8 (induced by source paper "exploiting sentential context for neural machine translation"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "dynamic layer aggregation for neural machine translation with routing by agreement"
    - "information aggregation for multi head attention with routing by agreement"
    - "context aware self attention networks"
    - "universal transformers"
    - "modeling recurrence for transformer"
    - "deconvolution based global decoding for neural machine translation"
    - "exploiting deep representations for neural machine translation"

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)