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: "a teacher student framework for zero resource neural machine translation"
  Abstract: While end-to-end neural machine translation (NMT) has made remarkable progress recently, it still suffers from the data scarcity problem for low-resource language pairs and domains. In this paper, we propose a method for zero-resource NMT by assuming that parallel sentences have close probabilities of generating a sentence in a third language. Based on this assumption, our method is able to train a source-to-target NMT model ("student") without parallel corpora available, guided by an existing pivot-to-target NMT model ("teacher") on a source-pivot parallel corpus. Experimental results show that the proposed method significantly improves over a baseline pivot-based model by +3.0 BLEU points across various language pairs.

Connected through hyperedge HE-1 (induced by source paper "a brief survey of multilingual neural machine translation"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "massively multilingual neural machine translation"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "the missing ingredient in zero shot neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "trivial transfer learning for low resource neural machine translation"
    - "zero resource multilingual model transfer learning what to share"

Connected through hyperedge HE-2 (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"
    - "massively multilingual neural machine translation in the wild findings and challenges"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "mix and match networks multi domain alignment for unpaired image to image translation"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "learning to adapt structured output space for semantic segmentation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "mix and match networks encoder decoder alignment for zero pair image translation"
    - "depth aware cnn for rgb d segmentation"
    - "multimodal unsupervised image to image translation"
    - "conditional image to image translation"
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"

Connected through hyperedge HE-4 (induced by source paper "a survey of multilingual neural machine translation"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "the missing ingredient in zero shot neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "trivial transfer learning for low resource neural machine translation"
    - "zero resource multilingual model transfer learning what to share"
    - "universal neural machine translation for extremely low resource languages"

Connected through hyperedge HE-5 (induced by source paper "effective cross lingual transfer of neural machine translation models without shared vocabularies"):
  with members:
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "improving unsupervised word by word translation with language model and denoising autoencoder"
    - "cross lingual language model pretraining"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "unsupervised statistical machine translation"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "trivial transfer learning for low resource neural machine translation"
    - "universal neural machine translation for extremely low resource languages"

Connected through hyperedge HE-6 (induced by source paper "consistency by agreement in zero shot neural machine translation"):
  with members:
    - "the missing ingredient in zero shot neural machine translation"
    - "tensor2tensor for neural machine translation"
    - "xnli evaluating cross lingual sentence representations"
    - "style transfer through back translation"
    - "large scale distributed neural network training through online distillation"
    - "a neural interlingua for multilingual machine translation"
    - "contextual parameter generation for universal neural machine translation"
    - "phrase based neural unsupervised machine translation"

Connected through hyperedge HE-7 (induced by source paper "revisiting low resource neural machine translation a case study"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "revisiting character based neural machine translation with capacity and compression"
    - "unsupervised statistical machine translation"
    - "trivial transfer learning for low resource neural machine translation"
    - "pervasive attention 2d convolutional neural networks for sequence to sequence prediction"
    - "universal neural machine translation for extremely low resource languages"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "a call for clarity in reporting bleu scores"

Connected through hyperedge HE-8 (induced by source paper "a study of multilingual neural machine translation"):
  with members:
    - "unsupervised pivot translation for distant languages"
    - "multilingual neural machine translation with knowledge distillation"
    - "mass masked sequence to sequence pre training for language generation"
    - "multilingual neural machine translation with language clustering"
    - "language graph distillation for low resource machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "universal neural machine translation for extremely low resource languages"
    - "parameter sharing methods for multilingual self attentional translation models"

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)