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: "densely connected attention propagation for reading comprehension"
  Abstract: We propose DecaProp (Densely Connected Attention Propagation), a new densely connected neural architecture for reading comprehension (RC). There are two distinct characteristics of our model. Firstly, our model densely connects all pairwise layers of the network, modeling relationships between passage and query across all hierarchical levels. Secondly, the dense connectors in our network are learned via attention instead of standard residual skip-connectors. To this end, we propose novel Bidirectional Attention Connectors (BAC) for efficiently forging connections throughout the network. We conduct extensive experiments on four challenging RC benchmarks. Our proposed approach achieves state-of-the-art results on all four, outperforming existing baselines by up to $2.6\%-14.2\%$ in absolute F1 score.

Connected through hyperedge HE-1 (induced by source paper "multi style generative reading comprehension"):
  with members:
    - "learning and evaluating general linguistic intelligence"
    - "a deep cascade model for multi document reading comprehension"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "the natural language decathlon multitask learning as question answering"
    - "bottom up abstractive summarization"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "duorc towards complex language understanding with paraphrased reading comprehension"

Connected through hyperedge HE-2 (induced by source paper "multiqa an empirical investigation of generalization and transfer in reading comprehension"):
  with members:
    - "multi step retriever reader interaction for scalable open domain question answering"
    - "learning and evaluating general linguistic intelligence"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "multi task deep neural networks for natural language understanding"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "an embarrassingly simple approach for transfer learning from pretrained language models"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"

Connected through hyperedge HE-3 (induced by source paper "a discrete hard em approach for weakly supervised question answering"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "learning to generalize from sparse and underspecified rewards"
    - "a comprehensive exploration on wikisql with table aware word contextualization"
    - "compositional questions do not necessitate multi hop reasoning"
    - "multi style generative reading comprehension"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "x sql reinforce schema representation with context"
    - "a bert baseline for the natural questions"

Connected through hyperedge HE-4 (induced by source paper "controlling risk of web question answering"):
  with members:
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "reducing network agnostophobia"
    - "confidence modeling for neural semantic parsing"
    - "know what you don t know unanswerable questions for squad"
    - "retrieve and read multi task learning of information retrieval and reading comprehension"
    - "bias reduced uncertainty estimation for deep neural classifiers"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "confidence from invariance to image transformations"

Connected through hyperedge HE-5 (induced by source paper "relation module for non answerable prediction on question answering"):
  with members:
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "pvrnet point view relation neural network for 3d shape recognition"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "pair2vec compositional word pair embeddings for cross sentence inference"
    - "deep contextualized word representations"
    - "coqa a conversational question answering challenge"
    - "compositional attention networks for machine reasoning"
    - "qanet combining local convolution with global self attention for reading comprehension"

Connected through hyperedge HE-6 (induced by source paper "multi hop reading comprehension across multiple documents by reasoning over heterogeneous graphs"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "global to local memory pointer networks for task oriented dialogue"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "neural models for reasoning over multiple mentions using coreference"
    - "graph convolutional networks for text classification"

Connected through hyperedge HE-7 (induced by source paper "select answer and explain interpretable multi hop reading comprehension over multiple documents"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "bag bi directional attention entity graph convolutional network for multi hop reasoning question answering"
    - "answering while summarizing multi task learning for multi hop qa with evidence extraction"
    - "understanding dataset design choices for multi hop reasoning"
    - "evidence sentence extraction for machine reading comprehension"
    - "multi hop reading comprehension across multiple documents by reasoning over heterogeneous graphs"
    - "roberta a robustly optimized bert pretraining approach"
    - "an interpretable reasoning network for multi relation question answering"

Connected through hyperedge HE-8 (induced by source paper "exploring machine reading comprehension with explicit knowledge"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "contextualized word representations 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)