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: "end to end open domain question answering with"
  Abstract: We demonstrate an end-to-end question answering system that integrates BERT with the open-source Anserini information retrieval toolkit. In contrast to most question answering and reading comprehension models today, which operate over small amounts of input text, our system integrates best practices from IR with a BERT-based reader to identify answers from a large corpus of Wikipedia articles in an end-to-end fashion. We report large improvements over previous results on a standard benchmark test collection, showing that fine-tuning pretrained BERT with SQuAD is sufficient to achieve high accuracy in identifying answer spans.

Connected through hyperedge HE-1 (induced by source paper "differentiable reasoning over a virtual knowledge base"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "bag bi directional attention entity graph convolutional network for multi hop reasoning question answering"
    - "explore propose and assemble an interpretable model for multi hop reading comprehension"
    - "pullnet open domain question answering with iterative retrieval on knowledge bases and text"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "neural query language a knowledge base query language for tensorflow"
    - "multi hop paragraph retrieval for open domain question answering"
    - "leveraging knowledge bases in lstms for improving machine reading"

Connected through hyperedge HE-2 (induced by source paper "conversations with documents an exploration of document centered assistance"):
  with members:
    - "data augmentation for bert fine tuning in open domain question answering"
    - "unsupervised question answering by cloze translation"
    - "huggingface s transformers state of the art natural language processing"
    - "rankqa neural question answering with answer re ranking"
    - "know what you don t know unanswerable questions for squad"
    - "coqa a conversational question answering challenge"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "improving question answering with external knowledge"):
  with members:
    - "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"
    - "kg 2 learning to reason science exam questions with contextual knowledge graph embeddings"

Connected through hyperedge HE-4 (induced by source paper "pretrained encyclopedia weakly supervised knowledge pretrained language model"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "language models as knowledge bases"
    - "latent retrieval for weakly supervised open domain question answering"
    - "data augmentation for bert fine tuning in open domain question answering"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "knowledge augmented language model and its application to unsupervised named entity recognition"
    - "knowledge enhanced contextual word representations"

Connected through hyperedge HE-5 (induced by source paper "don t paraphrase detect rapid and effective data collection for semantic parsing"):
  with members:
    - "learning to learn semantic parsers from natural language supervision"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "practical semantic parsing for spoken language understanding"
    - "building a neural semantic parser from a domain ontology"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "improving text to sql evaluation methodology"
    - "building a conversational agent overnight with dialogue self play"
    - "allennlp a deep semantic natural language processing platform"

Connected through hyperedge HE-6 (induced by source paper "pretrained ai models performativity mobility and change"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "on measuring social biases in sentence encoders"
    - "spanbert improving pre training by representing and predicting spans"
    - "patentbert patent classification with fine tuning a pre trained bert model"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "the ethics of ai ethics an evaluation of guidelines"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "discrimination in the age of algorithms"

Connected through hyperedge HE-7 (induced by source paper "retrieve read rerank towards end to end multi document reading comprehension"):
  with members:
    - "has qa hierarchical answer spans model for open domain question answering"
    - "a deep cascade model for multi document reading comprehension"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "read verify machine reading comprehension with unanswerable questions"

Connected through hyperedge HE-8 (induced by source paper "multi stage document ranking with bert"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "passage re ranking with bert"
    - "simple applications of bert for ad hoc document retrieval"
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "cedr contextualized embeddings for document ranking"
    - "query driven algorithm selection in early stage retrieval"

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)