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: "consensus attention based neural networks for chinese reading comprehension"
  Abstract: Reading comprehension has embraced a booming in recent NLP research. Several institutes have released the Cloze-style reading comprehension data, and these have greatly accelerated the research of machine comprehension. In this work, we firstly present Chinese reading comprehension datasets, which consist of People Daily news dataset and Children's Fairy Tale (CFT) dataset. Also, we propose a consensus attention-based neural network architecture to tackle the Cloze-style reading comprehension problem, which aims to induce a consensus attention over every words in the query. Experimental results show that the proposed neural network significantly outperforms the state-of-the-art baselines in several public datasets. Furthermore, we setup a baseline for Chinese reading comprehension task, and hopefully this would speed up the process for future research.

Connected through hyperedge HE-1 (induced by source paper "dnanet de normalized attention based multi resolution network for human pose estimation"):
  with members:
    - "convolutional spatial attention model for reading comprehension with multiple choice questions"
    - "spatial group wise enhance improving semantic feature learning in convolutional networks"
    - "deep high resolution representation learning for human pose estimation"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "pose flow efficient online pose tracking"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "look into person joint body parsing pose estimation network and a new benchmark"

Connected through hyperedge HE-2 (induced by source paper "investigating prior knowledge for challenging chinese machine reading comprehension"):
  with members:
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "ernie enhanced representation through knowledge integration"
    - "geosqa a benchmark for scenario based question answering in the geography domain at high school level"
    - "pre training with whole word masking for chinese bert"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "chid a large scale chinese idiom dataset for cloze test"
    - "analogical reasoning on chinese morphological and semantic relations"
    - "exploiting sentence embedding for medical question answering"

Connected through hyperedge HE-3 (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"
    - "a span extraction dataset for chinese machine reading comprehension"
    - "race large scale reading comprehension dataset from examinations"

Connected through hyperedge HE-4 (induced by source paper "xcmrc evaluating cross lingual machine reading comprehension"):
  with members:
    - "a corpus for multilingual document classification in eight languages"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "cjrc a reliable human annotated benchmark dataset for chinese judicial reading comprehension"):
  with members:
    - "cail2018 a large scale legal dataset for judgment prediction"
    - "overview of cail2018 legal judgment prediction competition"
    - "know what you don t know unanswerable questions for squad"
    - "neural entity reasoner for global consistency in ner"
    - "coqa a conversational question answering challenge"
    - "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-6 (induced by source paper "discriminative sentence modeling for story ending prediction"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "self normalizing neural networks"
    - "attention over attention neural networks for reading comprehension"
    - "the effect of different writing tasks on linguistic style a case study of the roc story cloze task"
    - "generating and exploiting large scale pseudo training data for zero pronoun resolution"
    - "bidirectional attention flow for machine comprehension"
    - "text understanding with the attention sum reader network"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-7 (induced by source paper "chid a large scale chinese idiom dataset for cloze test"):
  with members:
    - "coqa a conversational question answering challenge"
    - "race large scale reading comprehension dataset from examinations"
    - "large scale cloze test dataset created by teachers"
    - "evaluating machine translation performance on chinese idioms with a blacklist method"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "bidirectional attention flow for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"
    - "the lambada dataset word prediction requiring a broad discourse context"

Connected through hyperedge HE-8 (induced by source paper "contextual recurrent units for cloze style reading comprehension"):
  with members:
    - "dataset and neural recurrent sequence labeling model for open domain factoid question answering"
    - "reasoning with memory augmented neural networks for language comprehension"
    - "text understanding with the attention sum reader network"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"
    - "iterative alternating neural attention for machine reading"
    - "words or characters fine grained gating for reading comprehension"
    - "ac blstm asymmetric convolutional bidirectional lstm networks for text classification"
    - "effective approaches to attention based neural machine translation"

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)