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: "multilingual extractive reading comprehension by runtime machine translation"
  Abstract: Despite recent work in Reading Comprehension (RC), progress has been mostly limited to English due to the lack of large-scale datasets in other languages. In this work, we introduce the first RC system for languages without RC training data. Given a target language without RC training data and a pivot language with RC training data (e.g. English), our method leverages existing RC resources in the pivot language by combining a competitive RC model in the pivot language with an attentive Neural Machine Translation (NMT) model. We first translate the data from the target to the pivot language, and then obtain an answer using the RC model in the pivot language. Finally, we recover the corresponding answer in the original language using soft-alignment attention scores from the NMT model. We create evaluation sets of RC data in two non-English languages, namely Japanese and French, to evaluate our method. Experimental results on these datasets show that our method significantly outperforms a back-translation baseline of a state-of-the-art product-level machine translation system.

Connected through hyperedge HE-1 (induced by source paper "tydi qa a benchmark for information seeking question answering in typologically diverse languages"):
  with members:
    - "neural arabic question answering"
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "latent retrieval for weakly supervised open domain question answering"
    - "what question answering can learn from trivia nerds"
    - "question answering is a format when is it useful"
    - "korquad1 0 korean qa dataset for machine reading comprehension"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "mlqa evaluating cross lingual extractive question answering"

Connected through hyperedge HE-2 (induced by source paper "fquad french question answering dataset"):
  with members:
    - "unsupervised cross lingual representation learning at scale"
    - "camembert a tasty french language model"
    - "multilingual question answering from formatted text applied to conversational agents"
    - "mlqa evaluating cross lingual extractive question answering"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "a qualitative comparison of coqa squad 2 0 and quac"

Connected through hyperedge HE-3 (induced by source paper "cross lingual transfer learning for question answering"):
  with members:
    - "polyglot contextual representations improve crosslingual transfer"
    - "cross lingual language model pretraining"
    - "spoken squad a study of mitigating the impact of speech recognition errors on listening comprehension"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "drcd a chinese machine reading comprehension dataset"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "multilingual question answering from formatted text applied to conversational agents"):
  with members:
    - "beyond english only reading comprehension experiments in zero shot multilingual transfer for bulgarian"
    - "how multilingual is multilingual bert"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "roberta a robustly optimized bert pretraining approach"
    - "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"

Connected through hyperedge HE-5 (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-6 (induced by source paper "beyond english only reading comprehension experiments in zero shot multilingual transfer for bulgarian"):
  with members:
    - "passage re ranking with bert"
    - "improving question answering with external knowledge"
    - "transformer xl attentive language models beyond a fixed length context"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "massively multilingual neural machine translation"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"

Connected through hyperedge HE-7 (induced by source paper "xlda cross lingual data augmentation for natural language inference and question answering"):
  with members:
    - "cross lingual language model pretraining"
    - "xnli evaluating cross lingual sentence representations"
    - "universal neural machine translation for extremely low resource languages"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-8 (induced by source paper "bipar a bilingual parallel dataset for multilingual and cross lingual reading comprehension on novels"):
  with members:
    - "analogical reasoning on chinese morphological and semantic relations"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "coqa a conversational question answering challenge"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a span extraction dataset for chinese machine reading comprehension"
    - "race large scale reading comprehension dataset from examinations"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"

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)