human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "dc bert decoupling question and document for efficient contextual encoding"):
  Abstract: Recent studies on open-domain question answering have achieved prominent performance improvement using pre-trained language models such as BERT. State-of-the-art approaches typically follow the "retrieve and read" pipeline and employ BERT-based reranker to filter retrieved documents before feeding them into the reader module. The BERT retriever takes as input the concatenation of question and each retrieved document. Despite the success of these approaches in terms of QA accuracy, due to the concatenation, they can barely handle high-throughput of incoming questions each with a large collection of retrieved documents. To address the efficiency problem, we propose DC-BERT, a decoupled contextual encoding framework that has dual BERT models: an online BERT which encodes the question only once, and an offline BERT which pre-encodes all the documents and caches their encodings. On SQuAD Open and Natural Questions Open datasets, DC-BERT achieves 10x speedup on document retrieval, while retaining most (about 98%) of the QA performance compared to state-of-the-art approaches for open-domain question answering.

Includes members:
  Member "q8bert quantized 8bit bert":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adabert task adaptive bert compression with differentiable neural architecture search")
  Member "latent retrieval for weakly supervised open domain question answering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "differentiable reasoning over a virtual knowledge base")
  Member "distilbert a distilled version of bert smaller faster cheaper and lighter":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on contextual embeddings")
  Member "bert rediscovers the classical nlp pipeline":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on contextual embeddings")
  Member "retrieve read rerank towards end to end multi document reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to retrieve reasoning paths over wikipedia graph for question answering")
  Member "learning to retrieve reasoning paths over wikipedia graph for question answering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "differentiable reasoning over a virtual knowledge base")
  Member "a discrete hard em approach for weakly supervised question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "differentiable reasoning over a virtual knowledge base")
  Member "self assembling modular networks for interpretable multi hop reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unsupervised question decomposition for question answering")

Question: Please tell me which class the source paper 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)