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: "semi supervised learning for neural machine translation"
  Abstract: While end-to-end neural machine translation (NMT) has made remarkable progress recently, NMT systems only rely on parallel corpora for parameter estimation. Since parallel corpora are usually limited in quantity, quality, and coverage, especially for low-resource languages, it is appealing to exploit monolingual corpora to improve NMT. We propose a semi-supervised approach for training NMT models on the concatenation of labeled (parallel corpora) and unlabeled (monolingual corpora) data. The central idea is to reconstruct the monolingual corpora using an autoencoder, in which the source-to-target and target-to-source translation models serve as the encoder and decoder, respectively. Our approach can not only exploit the monolingual corpora of the target language, but also of the source language. Experiments on the Chinese-English dataset show that our approach achieves significant improvements over state-of-the-art SMT and NMT systems.

Connected through hyperedge HE-1 (induced by source paper "bi decoder augmented network for neural machine translation"):
  with members:
    - "reinforced dynamic reasoning for conversational question generation"
    - "discourse marker augmented network with reinforcement learning for natural language inference"
    - "macnet transferring knowledge from machine comprehension to sequence to sequence models"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "attention is all you need"
    - "unsupervised neural machine translation"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "what do neural machine translation models learn about morphology"

Connected through hyperedge HE-2 (induced by source paper "self training with noisy student improves imagenet classification"):
  with members:
    - "using videos to evaluate image model robustness"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "semi supervised segmentation of salt bodies in seismic images using an ensemble of convolutional neural networks"
    - "robustness properties of facebook s resnext wsl models"
    - "billion scale semi supervised learning for image classification"
    - "pseudo labeling and confirmation bias in deep semi supervised learning"
    - "natural adversarial examples"
    - "leveraging just a few keywords for fine grained aspect detection through weakly supervised co training"

Connected through hyperedge HE-3 (induced by source paper "unsupervised data augmentation for consistency training"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "semi supervised learning by label gradient alignment"
    - "randaugment practical data augmentation with no separate search"
    - "mixture models for diverse machine translation tricks of the trade"
    - "are labels required for improving adversarial robustness"
    - "adversarially robust generalization just requires more unlabeled data"
    - "data efficient image recognition with contrastive predictive coding"

Connected through hyperedge HE-4 (induced by source paper "two headed monster and crossed co attention networks"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "the evolved transformer"
    - "context aware self attention networks"
    - "modeling recurrence for transformer"
    - "adaptive attention span in transformers"
    - "scaling neural machine translation"
    - "modeling localness for self attention networks"

Connected through hyperedge HE-5 (induced by source paper "a survey of methods to leverage monolingual data in low resource neural machine translation"):
  with members:
    - "a survey of domain adaptation for neural machine translation"
    - "simple fusion return of the language model"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "phrase based neural unsupervised machine translation"
    - "attention is all you need"
    - "unsupervised neural machine translation"
    - "cold fusion training seq2seq models together with language models"
    - "unsupervised pretraining for sequence to sequence learning"

Connected through hyperedge HE-6 (induced by source paper "transformation of dense and sparse text representations"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "consistent dialogue generation with self supervised feature learning"
    - "xsense learning sense separated sparse representations and textual definitions for explainable word sense networks"
    - "investigating capsule networks with dynamic routing for text classification"
    - "attention is all you need"
    - "sparse representation based open set recognition"
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "spine sparse interpretable neural embeddings"

Connected through hyperedge HE-7 (induced by source paper "on semi supervised multiple representation behavior learning"):
  with members:
    - "attributed rhetorical structure grammar for domain text summarization"
    - "content selection in deep learning models of summarization"
    - "a simple theoretical model of importance for summarization"
    - "semi supervised representation learning based on probabilistic labeling"
    - "neural summarization by extracting sentences and words"
    - "semi supervised classification with graph convolutional networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "generalizing skills with semi supervised reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "unpaired image captioning by language pivoting"):
  with members:
    - "shuffle then assemble learning object agnostic visual relationship features"
    - "zero resource neural machine translation with multi agent communication game"
    - "texygen a benchmarking platform for text generation models"
    - "improved image captioning via policy gradient optimization of spider"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "ai challenger a large scale dataset for going deeper in image understanding"
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"

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)