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: "hfl rc system at semeval 2018 task 11 hybrid multi aspects model for commonsense reading comprehension"
  Abstract: This paper describes the system which got the state-of-the-art results at SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. In this paper, we present a neural network called Hybrid Multi-Aspects (HMA) model, which mimic the human's intuitions on dealing with the multiple-choice reading comprehension. In this model, we aim to produce the predictions in multiple aspects by calculating attention among the text, question and choices, and combine these results for final predictions. Experimental results show that our HMA model could give substantial improvements over the baseline system and got the first place on the final test set leaderboard with the accuracy of 84.13%.

Connected through hyperedge HE-1 (induced by source paper "convolutional spatial attention model for reading comprehension with multiple choice questions"):
  with members:
    - "eliminet a model for eliminating options for reading comprehension with multiple choice questions"
    - "deep contextualized word representations"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "reinforced mnemonic reader for machine comprehension"
    - "race large scale reading comprehension dataset from examinations"
    - "attention over attention neural networks for reading comprehension"
    - "fusionnet fusing via fully aware attention with application to machine comprehension"
    - "dynamic fusion networks for machine reading comprehension"

Connected through hyperedge HE-2 (induced by source paper "multi perspective fusion network for commonsense reading comprehension"):
  with members:
    - "mcscript a novel dataset for assessing machine comprehension using script knowledge"
    - "race large scale reading comprehension dataset from examinations"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "learning recurrent span representations for extractive question answering"
    - "fusionnet fusing via fully aware attention with application to machine comprehension"
    - "dcn mixed objective and deep residual coattention for question answering"
    - "newsqa a machine comprehension dataset"
    - "a decomposable attention model for natural language inference"

Connected through hyperedge HE-3 (induced by source paper "incorporating relation knowledge into commonsense reading comprehension with multi task learning"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "yuanfudao at semeval 2018 task 11 three way attention and relational knowledge for commonsense machine comprehension"
    - "improving question answering by commonsense based pre training"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "commonsense for generative multi hop question answering tasks"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"

Connected through hyperedge HE-4 (induced by source paper "improving machine reading comprehension with general reading strategies"):
  with members:
    - "eliminet a model for eliminating options for reading comprehension with multiple choice questions"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "generating wikipedia by summarizing long sequences"
    - "what makes reading comprehension questions easier"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "coqa a conversational question answering challenge"
    - "multi range reasoning for machine comprehension"
    - "qanet combining local convolution with global self attention for reading comprehension"

Connected through hyperedge HE-5 (induced by source paper "improving question answering by commonsense based pre training"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "yuanfudao at semeval 2018 task 11 three way attention and relational knowledge for commonsense machine comprehension"
    - "a systematic classification of knowledge reasoning and context within the arc dataset"
    - "learning beyond datasets knowledge graph augmented neural networks for natural language processing"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "kg 2 learning to reason science exam questions with contextual knowledge graph embeddings"
    - "think you have solved question answering try arc the ai2 reasoning challenge"

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)