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 corpus for multilingual document classification in eight languages"
  Abstract: Cross-lingual document classification aims at training a document classifier on resources in one language and transferring it to a different language without any additional resources. Several approaches have been proposed in the literature and the current best practice is to evaluate them on a subset of the Reuters Corpus Volume 2. However, this subset covers only few languages (English, German, French and Spanish) and almost all published works focus on the the transfer between English and German. In addition, we have observed that the class prior distributions differ significantly between the languages. We argue that this complicates the evaluation of the multilinguality. In this paper, we propose a new subset of the Reuters corpus with balanced class priors for eight languages. By adding Italian, Russian, Japanese and Chinese, we cover languages which are very different with respect to syntax, morphology, etc. We provide strong baselines for all language transfer directions using multilingual word and sentence embeddings respectively. Our goal is to offer a freely available framework to evaluate cross-lingual document classification, and we hope to foster by these means, research in this important area.

Connected through hyperedge HE-1 (induced by source paper "beto bentz becas the surprising cross lingual effectiveness of bert"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "polyglot contextual representations improve crosslingual transfer"
    - "cross lingual language model pretraining"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "near or far wide range zero shot cross lingual dependency parsing"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "mlqa evaluating cross lingual extractive question answering"):
  with members:
    - "neural arabic question answering"
    - "cross lingual transfer learning for question answering"
    - "beyond english only reading comprehension experiments in zero shot multilingual transfer for bulgarian"
    - "unsupervised question answering by cloze translation"
    - "synthetic qa corpora generation with roundtrip consistency"
    - "xcmrc evaluating cross lingual machine reading comprehension"
    - "korquad1 0 korean qa dataset for machine reading comprehension"
    - "xlda cross lingual data augmentation for natural language inference and question answering"

Connected through hyperedge HE-3 (induced by source paper "on the cross lingual transferability of monolingual representations"):
  with members:
    - "multilingual alignment of contextual word representations"
    - "parameter efficient transfer learning for nlp"
    - "unicoder a universal language encoder by pre training with multiple cross lingual tasks"
    - "paws paraphrase adversaries from word scrambling"
    - "cross lingual ability of multilingual bert an empirical study"
    - "bilingual lexicon induction through unsupervised machine translation"
    - "wic the word in context dataset for evaluating context sensitive meaning representations"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"

Connected through hyperedge HE-4 (induced by source paper "robust cross lingual embeddings from parallel sentences"):
  with members:
    - "crosslingual document embedding as reduced rank ridge regression"
    - "a survey of cross lingual word embedding models"
    - "analyzing the limitations of cross lingual word embedding mappings"
    - "better word embeddings by disentangling contextual n gram information"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "unsupervised multilingual word embeddings"
    - "non adversarial unsupervised word translation"

Connected through hyperedge HE-5 (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-6 (induced by source paper "scalable cross lingual transfer of neural sentence embeddings"):
  with members:
    - "context aware cross lingual mapping"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "evaluation of unsupervised compositional representations"
    - "evaluating compositionality in sentence embeddings"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "learning joint multilingual sentence representations with neural machine translation"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "unsupervised word mapping using structural similarities in monolingual embeddings"

Connected through hyperedge HE-7 (induced by source paper "xcmrc evaluating cross lingual machine reading comprehension"):
  with members:
    - "xnli evaluating cross lingual sentence representations"
    - "know what you don t know unanswerable questions for squad"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an empirical analysis of nmt derived interlingual embeddings and their use in parallel sentence identification"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "race large scale reading comprehension dataset from examinations"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "searchqa a new q a dataset augmented with context from a search engine"

Connected through hyperedge HE-8 (induced by source paper "bridging the domain gap in cross lingual document classification"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "a survey of cross lingual word embedding models"
    - "unsupervised data augmentation for consistency training"
    - "cross lingual language model pretraining"
    - "unsupervised domain adaptation of contextualized embeddings for sequence labeling"
    - "an embarrassingly simple approach for transfer learning from pretrained language models"
    - "pytext a seamless path from nlp research to production"
    - "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)