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: "answering science exam questions using query rewriting with background knowledge"
  Abstract: Open-domain question answering (QA) is an important problem in AI and NLP that is emerging as a bellwether for progress on the generalizability of AI methods and techniques. Much of the progress in open-domain QA systems has been realized through advances in information retrieval methods and corpus construction. In this paper, we focus on the recently introduced ARC Challenge dataset, which contains 2,590 multiple choice questions authored for grade-school science exams. These questions are selected to be the most challenging for current QA systems, and current state of the art performance is only slightly better than random chance. We present a system that rewrites a given question into queries that are used to retrieve supporting text from a large corpus of science-related text. Our rewriter is able to incorporate background knowledge from ConceptNet and -- in tandem with a generic textual entailment system trained on SciTail that identifies support in the retrieved results -- outperforms several strong baselines on the end-to-end QA task despite only being trained to identify essential terms in the original source question. We use a generalizable decision methodology over the retrieved evidence and answer candidates to select the best answer. By combining query rewriting, background knowledge, and textual entailment our system is able to outperform several strong baselines on the ARC dataset.

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"
    - "kg 2 learning to reason science exam questions with contextual knowledge graph embeddings"
    - "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 "commonsense knowledge base completion with structural and semantic context"):
  with members:
    - "commonsense reasoning for natural language understanding a survey of benchmarks resources and approaches"
    - "language models as knowledge bases"
    - "probing the need for visual context in multimodal machine translation"
    - "commonsense knowledge mining from pretrained models"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "commonsense mining as knowledge base completion a study on the impact of novelty"
    - "end to end structure aware convolutional networks for knowledge base completion"

Connected through hyperedge HE-4 (induced by source paper "repurposing entailment for multi hop question answering tasks"):
  with members:
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "allennlp a deep semantic natural language processing platform"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "deep contextualized word representations"
    - "improving machine reading comprehension with general reading strategies"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "race large scale reading comprehension dataset from examinations"

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.AI(Artificial Intelligence)