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: "zero shot cross lingual classification using multilingual neural machine translation"
  Abstract: Transferring representations from large supervised tasks to downstream tasks has shown promising results in AI fields such as Computer Vision and Natural Language Processing (NLP). In parallel, the recent progress in Machine Translation (MT) has enabled one to train multilingual Neural MT (NMT) systems that can translate between multiple languages and are also capable of performing zero-shot translation. However, little attention has been paid to leveraging representations learned by a multilingual NMT system to enable zero-shot multilinguality in other NLP tasks. In this paper, we demonstrate a simple framework, a multilingual Encoder-Classifier, for cross-lingual transfer learning by reusing the encoder from a multilingual NMT system and stitching it with a task-specific classifier component. Our proposed model achieves significant improvements in the English setup on three benchmark tasks - Amazon Reviews, SST and SNLI. Further, our system can perform classification in a new language for which no classification data was seen during training, showing that zero-shot classification is possible and remarkably competitive. In order to understand the underlying factors contributing to this finding, we conducted a series of analyses on the effect of the shared vocabulary, the training data type for NMT, classifier complexity, encoder representation power, and model generalization on zero-shot performance. Our results provide strong evidence that the representations learned from multilingual NMT systems are widely applicable across languages and tasks.

Connected through hyperedge HE-1 (induced by source paper "investigating multilingual nmt representations at scale"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "consistency by agreement in zero shot neural machine translation"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "multilingual neural machine translation with language clustering"
    - "cross lingual language model pretraining"
    - "massively multilingual neural machine translation"
    - "the missing ingredient in zero shot neural machine translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

Connected through hyperedge HE-2 (induced by source paper "massively multilingual neural machine translation"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "achieving human parity on automatic chinese to english news translation"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "universal neural machine translation for extremely low resource languages"
    - "parameter sharing methods for multilingual self attentional translation models"
    - "emerging language spaces learned from massively multilingual corpora"
    - "scaling neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "evaluating the cross lingual effectiveness of massively multilingual neural machine translation"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "a survey of cross lingual word embedding models"
    - "polyglot contextual representations improve crosslingual transfer"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "how multilingual is multilingual bert"
    - "cross lingual language model pretraining"
    - "massively multilingual neural machine translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

Connected through hyperedge HE-4 (induced by source paper "towards interlingua neural machine translation"):
  with members:
    - "the missing ingredient in zero shot neural machine translation"
    - "unsupervised statistical machine translation"
    - "xnli evaluating cross lingual sentence representations"
    - "fast decoding in sequence models using discrete latent variables"
    - "a neural interlingua for multilingual machine translation"
    - "phrase based neural unsupervised machine translation"
    - "an empirical analysis of nmt derived interlingual embeddings and their use in parallel sentence identification"
    - "unsupervised machine translation using monolingual corpora only"

Connected through hyperedge HE-5 (induced by source paper "cross lingual language model pretraining"):
  with members:
    - "xnli evaluating cross lingual sentence representations"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised cross lingual word embedding by multilingual neural language models"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "character level language modeling with deeper self attention"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"

Connected through hyperedge HE-6 (induced by source paper "machine translation for machines the sentiment classification use case"):
  with members:
    - "semeval 2013 task 2 sentiment analysis in twitter"
    - "can neural machine translation be improved with user feedback"
    - "a study of reinforcement learning for neural machine translation"
    - "getting gender right in neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "six challenges for neural machine translation"
    - "reinforcement learning for bandit neural machine translation with simulated human feedback"

Connected through hyperedge HE-7 (induced by source paper "aligning cross lingual entities with multi aspect information"):
  with members:
    - "matching entities across different knowledge graphs with graph embeddings"
    - "densely connected graph convolutional networks for graph to sequence learning"
    - "simple attention based representation learning for ranking short social media posts"
    - "attention guided graph convolutional networks for relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "xnli evaluating cross lingual sentence representations"
    - "machine translation using semantic web technologies a survey"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"

Connected through hyperedge HE-8 (induced by source paper "cross lingual transfer learning for multilingual task oriented dialog"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "polyglot contextual representations improve crosslingual transfer"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "pytext a seamless path from nlp research to production"
    - "on the strength of character language models for multilingual named entity recognition"
    - "parameter sharing between dependency parsers for related languages"
    - "xnli evaluating cross lingual sentence 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)