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: "adaptive document retrieval for deep question answering"
  Abstract: State-of-the-art systems in deep question answering proceed as follows: (1) an initial document retrieval selects relevant documents, which (2) are then processed by a neural network in order to extract the final answer. Yet the exact interplay between both components is poorly understood, especially concerning the number of candidate documents that should be retrieved. We show that choosing a static number of documents -- as used in prior research -- suffers from a noise-information trade-off and yields suboptimal results. As a remedy, we propose an adaptive document retrieval model. This learns the optimal candidate number for document retrieval, conditional on the size of the corpus and the query. We report extensive experimental results showing that our adaptive approach outperforms state-of-the-art methods on multiple benchmark datasets, as well as in the context of corpora with variable sizes.

Connected through hyperedge HE-1 (induced by source paper "a study on efficiency accuracy and document structure for answer sentence selection"):
  with members:
    - "passage re ranking with bert"
    - "tanda transfer and adapt pre trained transformer models for answer sentence selection"
    - "understanding the behaviors of bert in ranking"
    - "a gated self attention memory network for answer selection"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "energy and policy considerations for deep learning in nlp"
    - "roberta a robustly optimized bert pretraining approach"
    - "hyperbolic representation learning for fast and efficient neural question answering"

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"
    - "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-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 "reqa an evaluation for end to end answer retrieval models"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "training neural response selection for task oriented dialogue systems"
    - "improving multilingual sentence embedding using bi directional dual encoder with additive margin softmax"
    - "multilingual universal sentence encoder for semantic retrieval"
    - "learning semantic textual similarity from conversations"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "end to end retrieval in continuous space"

Connected through hyperedge HE-5 (induced by source paper "answering complex open domain questions through iterative query generation"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "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"
    - "document expansion by query prediction"
    - "understanding dataset design choices for multi hop reasoning"
    - "multi hop reading comprehension through question decomposition and rescoring"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"

Connected through hyperedge HE-6 (induced by source paper "movie plot analysis via turning point identification"):
  with members:
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "universal sentence encoder"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "wikipedia2vec an optimized tool for learning embeddings of words and entities from wikipedia"
    - "whodunnit crime drama as a case for natural language understanding"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"

Connected through hyperedge HE-7 (induced by source paper "putting question answering systems into practice transfer learning for efficient domain customization"):
  with members:
    - "deep learning in business analytics and operations research models applications and managerial implications"
    - "deep learning for affective computing text based emotion recognition in decision support"
    - "deep bayesian active learning for natural language processing results of a large scale empirical study"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "decision support from financial disclosures with deep neural networks and transfer learning"
    - "reading wikipedia to answer open domain questions"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "bidirectional attention flow for machine 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"
    - "ranking paragraphs for improving answer recall in open domain question answering"
    - "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"
    - "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)