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: "kg 2 learning to reason science exam questions with contextual knowledge graph embeddings"
  Abstract: The AI2 Reasoning Challenge (ARC), a new benchmark dataset for question answering (QA) has been recently released. ARC only contains natural science questions authored for human exams, which are hard to answer and require advanced logic reasoning. On the ARC Challenge Set, existing state-of-the-art QA systems fail to significantly outperform random baseline, reflecting the difficult nature of this task. In this paper, we propose a novel framework for answering science exam questions, which mimics human solving process in an open-book exam. To address the reasoning challenge, we construct contextual knowledge graphs respectively for the question itself and supporting sentences. Our model learns to reason with neural embeddings of both knowledge graphs. Experiments on the ARC Challenge Set show that our model outperforms the previous state-of-the-art QA systems.

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"
    - "yuanfudao at semeval 2018 task 11 three way attention and relational knowledge for commonsense machine comprehension"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "careful selection of knowledge to solve open book question answering"):
  with members:
    - "declarative question answering over knowledge bases containing natural language text with answer set programming"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "transforming question answering datasets into natural language inference datasets"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "answering science exam questions using query rewriting with background knowledge"
    - "coqa a conversational question answering challenge"
    - "quarel a dataset and models for answering questions about qualitative relationships"
    - "improving machine reading comprehension with general reading strategies"

Connected through hyperedge HE-3 (induced by source paper "multi domain dialogue state tracking as dynamic knowledge graph enhanced question answering"):
  with members:
    - "multiwoz 2 1 multi domain dialogue state corrections and state tracking baselines"
    - "sumbt slot utterance matching for universal and scalable belief tracking"
    - "towards scalable multi domain conversational agents the schema guided dialogue dataset"
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "towards universal dialogue state tracking"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"

Connected through hyperedge HE-4 (induced by source paper "answering questions by learning to rank learning to rank by answering questions"):
  with members:
    - "improving question answering with external knowledge"
    - "improving retrieval based question answering with deep inference models"
    - "know what you don t know unanswerable questions for squad"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "deep contextualized word representations"
    - "improving machine reading comprehension with general reading strategies"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning to attend on essential terms an enhanced retriever reader model for open domain question answering"

Connected through hyperedge HE-5 (induced by source paper "answering science exam questions using query rewriting with background knowledge"):
  with members:
    - "query expansion techniques for information retrieval a survey"
    - "improving question answering by commonsense based pre training"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "know what you don t know unanswerable questions for squad"
    - "end task oriented textual entailment via deep explorations of inter sentence interactions"
    - "improving natural language inference using external knowledge in the science questions domain"
    - "improving machine reading comprehension with general reading strategies"
    - "ncrf an open source neural sequence labeling toolkit"

Connected through hyperedge HE-6 (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"
    - "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"

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.LG(Machine Learning)