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: "yuanfudao at semeval 2018 task 11 three way attention and relational knowledge for commonsense machine comprehension"
  Abstract: This paper describes our system for SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. We use Three-way Attentive Networks (TriAN) to model interactions between the passage, question and answers. To incorporate commonsense knowledge, we augment input with relation embedding from the graph of general knowledge ConceptNet (Speer et al., 2017). As a result, our system achieves 2nd place with 83.95% accuracy on the official test data. Code is publicly available at this https URL

Connected through hyperedge HE-1 (induced by source paper "improving question answering with external knowledge"):
  with members:
    - "end to end open domain question answering with"
    - "improving retrieval based question answering with deep inference models"
    - "improving question answering by commonsense based pre training"
    - "ranking paragraphs for improving answer recall in open domain question answering"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "generating wikipedia by summarizing long sequences"
    - "what makes reading comprehension questions easier"
    - "kg 2 learning to reason science exam questions with contextual knowledge graph embeddings"

Connected through hyperedge HE-2 (induced by source paper "cosmos qa machine reading comprehension with contextual commonsense reasoning"):
  with members:
    - "bertscore evaluating text generation with bert"
    - "socialiqa commonsense reasoning about social interactions"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "dual co matching network for multi choice reading comprehension"
    - "modeling naive psychology of characters in simple commonsense stories"
    - "annotation artifacts in natural language inference data"
    - "mcscript a novel dataset for assessing machine comprehension using script knowledge"
    - "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"

Connected through hyperedge HE-3 (induced by source paper "big mood relating transformers to explicit commonsense knowledge"):
  with members:
    - "ernie enhanced representation through knowledge integration"
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "transformer xl attentive language models beyond a fixed length context"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "energy and policy considerations for deep learning in nlp"
    - "roberta a robustly optimized bert pretraining approach"
    - "mcscript a novel dataset for assessing machine comprehension using script knowledge"
    - "deep contextualized word representations"

Connected through hyperedge HE-4 (induced by source paper "teaching pretrained models with commonsense reasoning a preliminary kb based approach"):
  with members:
    - "incorporating external knowledge into machine reading for generative question answering"
    - "kagnet knowledge aware graph networks for commonsense reasoning"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cosmos qa machine reading comprehension with contextual commonsense reasoning"
    - "align mask and select a simple method for incorporating commonsense knowledge into language representation models"
    - "improving question answering by commonsense based pre training"
    - "a simple method for commonsense reasoning"

Connected through hyperedge HE-5 (induced by source paper "integrate image representation to text model on sentence level a semi supervised framework"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "discourse marker augmented network with reinforcement learning for natural language inference"
    - "i know what you want semantic learning for text comprehension"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning visually grounded sentence representations"
    - "vse improving visual semantic embeddings with hard negatives"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (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"
    - "improving question answering by commonsense based pre training"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "hfl rc system at semeval 2018 task 11 hybrid multi aspects model for commonsense reading comprehension"
    - "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-7 (induced by source paper "improving question answering by commonsense based pre training"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "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"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"

Connected through hyperedge HE-8 (induced by source paper "learning to attend on essential terms an enhanced retriever reader model for open domain question answering"):
  with members:
    - "improving question answering by commonsense based pre training"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "multi range reasoning for machine comprehension"
    - "improving machine reading comprehension with general reading strategies"
    - "retrieve and read multi task learning of information retrieval and reading comprehension"
    - "race large scale reading comprehension dataset from examinations"
    - "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)