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: "ranking paragraphs for improving answer recall in open domain question answering"
  Abstract: Recently, open-domain question answering (QA) has been combined with machine comprehension models to find answers in a large knowledge source. As open-domain QA requires retrieving relevant documents from text corpora to answer questions, its performance largely depends on the performance of document retrievers. However, since traditional information retrieval systems are not effective in obtaining documents with a high probability of containing answers, they lower the performance of QA systems. Simply extracting more documents increases the number of irrelevant documents, which also degrades the performance of QA systems. In this paper, we introduce Paragraph Ranker which ranks paragraphs of retrieved documents for a higher answer recall with less noise. We show that ranking paragraphs and aggregating answers using Paragraph Ranker improves performance of open-domain QA pipeline on the four open-domain QA datasets by 7.8% on average.

Connected through hyperedge HE-1 (induced by source paper "dc bert decoupling question and document for efficient contextual encoding"):
  with members:
    - "q8bert quantized 8bit bert"
    - "latent retrieval for weakly supervised open domain question answering"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "bert rediscovers the classical nlp pipeline"
    - "retrieve read rerank towards end to end multi document reading comprehension"
    - "learning to retrieve reasoning paths over wikipedia graph for question answering"
    - "a discrete hard em approach for weakly supervised question answering"
    - "self assembling modular networks for interpretable multi hop reasoning"

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "learning to retrieve reasoning paths over wikipedia graph for question answering"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "passage re ranking with bert"
    - "do multi hop readers dream of reasoning chains"
    - "pullnet open domain question answering with iterative retrieval on knowledge bases and text"
    - "compositional questions do not necessitate multi hop reasoning"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "multi hop paragraph retrieval for open domain question answering"
    - "retrieve read rerank towards end to end multi document reading comprehension"

Connected through hyperedge HE-4 (induced by source paper "multi hop paragraph retrieval for open domain question answering"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "a deep cascade model for multi document reading comprehension"
    - "end to end open domain question answering with"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "the web as a knowledge base for answering complex questions"
    - "neural models for reasoning over multiple mentions using coreference"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"

Connected through hyperedge HE-5 (induced by source paper "revealing the importance of semantic retrieval for machine reading at scale"):
  with members:
    - "cognitive graph for multi hop reading comprehension at scale"
    - "combining fact extraction and verification with neural semantic matching networks"
    - "automated fact checking task formulations methods and future directions"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "ukp athene multi sentence textual entailment for claim verification"
    - "deep contextualized word representations"
    - "fever a large scale dataset for fact extraction and verification"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "contextualized sparse representation with rectified n gram attention for open domain question answering"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "a discrete hard em approach for weakly supervised question answering"
    - "multi passage bert a globally normalized bert model for open domain question answering"
    - "rankqa neural question answering with answer re ranking"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "attention is all you need"
    - "zero shot relation extraction via reading comprehension"

Connected through hyperedge HE-7 (induced by source paper "real time open domain question answering with dense sparse phrase index"):
  with members:
    - "end to end open domain question answering with"
    - "weaver deep co encoding of questions and documents for machine reading"
    - "deep contextualized word representations"
    - "retrieve and read multi task learning of information retrieval and reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "billion scale similarity search with gpus"
    - "zero shot relation extraction via reading comprehension"

Connected through hyperedge HE-8 (induced by source paper "latent retrieval for weakly supervised open domain question answering"):
  with members:
    - "end to end open domain question answering with"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an efficient framework for learning sentence representations"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "adaptive document retrieval for deep question answering"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "searchqa a new q a dataset augmented with context from a search engine"

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)