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: "convolutional spatial attention model for reading comprehension with multiple choice questions"
  Abstract: Machine Reading Comprehension (MRC) with multiplechoice questions requires the machine to read given passage and select the correct answer among several candidates. In this paper, we propose a novel approach called Convolutional Spatial Attention (CSA) model which can better handle the MRC with multiple-choice questions. The proposed model could fully extract the mutual information among the passage, question, and the candidates, to form the enriched representations. Furthermore, to merge various attention results, we propose to use convolutional operation to dynamically summarize the attention values within the different size of regions. Experimental results show that the proposed model could give substantial improvements over various state-of- the-art systems on both RACE and SemEval-2018 Task11 datasets.

Connected through hyperedge HE-1 (induced by source paper "gennet reading comprehension with multiple choice questions using generation and selection model"):
  with members:
    - "eliminet a model for eliminating options for reading comprehension with multiple choice questions"
    - "option comparison network for multiple choice reading comprehension"
    - "dual co matching network for multi choice reading comprehension"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "a co matching model for multi choice reading comprehension"
    - "race large scale reading comprehension dataset from examinations"
    - "s net from answer extraction to answer generation for machine reading comprehension"
    - "a thorough examination of the cnn daily mail reading comprehension task"

Connected through hyperedge HE-2 (induced by source paper "dnanet de normalized attention based multi resolution network for human pose estimation"):
  with members:
    - "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"
    - "simple baselines for human pose estimation and tracking"

Connected through hyperedge HE-3 (induced by source paper "jec qa a legal domain question answering dataset"):
  with members:
    - "interpretable charge predictions for criminal cases learning to generate court views from fact descriptions"
    - "cail2018 a large scale legal dataset for judgment prediction"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "know what you don t know unanswerable questions for squad"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "secaps a sequence enhanced capsule model for charge prediction"

Connected through hyperedge HE-4 (induced by source paper "dual co matching network for multi choice reading comprehension"):
  with members:
    - "semantics aware bert for language understanding"
    - "hierarchical contextualized representation for named entity recognition"
    - "option comparison network for multiple choice reading comprehension"
    - "sg net syntax guided machine reading comprehension"
    - "evidence sentence extraction for machine reading comprehension"
    - "lattice based transformer encoder for neural machine translation"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "story ending prediction by transferable bert"

Connected through hyperedge HE-5 (induced by source paper "triplenet triple attention network for multi turn response selection in retrieval based chatbots"):
  with members:
    - "deep contextualized word representations"
    - "a structured self attentive sentence embedding"
    - "bidirectional attention flow for machine comprehension"
    - "on the evaluation of dialogue systems with next utterance classification"
    - "sentence pair scoring towards unified framework for text comprehension"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"
    - "match srnn modeling the recursive matching structure with spatial rnn"
    - "syntax based deep matching of short texts"

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)