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 span extraction dataset for chinese machine reading comprehension"
  Abstract: Machine Reading Comprehension (MRC) has become enormously popular recently and has attracted a lot of attention. However, the existing reading comprehension datasets are mostly in English. In this paper, we introduce a Span-Extraction dataset for Chinese Machine Reading Comprehension to add language diversities in this area. The dataset is composed by near 20,000 real questions annotated by human on Wikipedia paragraphs. We also annotated a challenge set which contains the questions that need comprehensive understanding and multi-sentence inference throughout the context. With the release of the dataset, we hosted the Second Evaluation Workshop on Chinese Machine Reading Comprehension (CMRC 2018). We hope the release of the dataset could further accelerate the machine reading comprehension research in Chinese language. The data is available through: this https URL

Connected through hyperedge HE-1 (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-2 (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"
    - "multilingual extractive reading comprehension by runtime machine translation"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "race large scale reading comprehension dataset from examinations"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "ernie 2 0 a continual pre training framework for language understanding"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "ernie enhanced representation through knowledge integration"
    - "multi task deep neural networks for natural language understanding"
    - "transformer xl attentive language models beyond a fixed length context"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "mining discourse markers for unsupervised sentence representation learning"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"

Connected through hyperedge HE-4 (induced by source paper "cross lingual machine reading comprehension"):
  with members:
    - "data augmentation for bert fine tuning in open domain question answering"
    - "towards robust neural machine translation"
    - "multilingual extractive reading comprehension by runtime machine translation"
    - "drcd a chinese machine reading comprehension dataset"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "read verify machine reading comprehension with unanswerable questions"
    - "attention is all you need"
    - "generating and exploiting large scale pseudo training data for zero pronoun resolution"

Connected through hyperedge HE-5 (induced by source paper "pre training with whole word masking for chinese bert"):
  with members:
    - "option comparison network for multiple choice reading comprehension"
    - "ernie enhanced representation through knowledge integration"
    - "transformer xl attentive language models beyond a fixed length context"
    - "ernie enhanced language representation with informative entities"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "cjrc a reliable human annotated benchmark dataset for chinese judicial reading comprehension"
    - "dual co matching network for multi choice reading comprehension"
    - "know what you don t know unanswerable questions for squad"

Connected through hyperedge HE-6 (induced by source paper "nezha neural contextualized representation for chinese language understanding"):
  with members:
    - "ernie enhanced representation through knowledge integration"
    - "transformer xl attentive language models beyond a fixed length context"
    - "ernie enhanced language representation with informative entities"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "ernie 2 0 a continual pre training framework for language understanding"
    - "pre training with whole word masking for chinese bert"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-7 (induced by source paper "sogou machine reading comprehension toolkit"):
  with members:
    - "a qualitative comparison of coqa squad 2 0 and quac"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"
    - "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"
    - "attention is all you need"
    - "learned in translation contextualized word vectors"

Connected through hyperedge HE-8 (induced by source paper "data augmentation for bert fine tuning in open domain question answering"):
  with members:
    - "end to end open domain question answering with"
    - "ranking paragraphs for improving answer recall in open domain question answering"
    - "deep contextualized word representations"
    - "drcd a chinese machine reading comprehension dataset"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adaptive document retrieval for deep question answering"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "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)