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 "contextualized sparse representation with rectified n gram attention for open domain question answering"):
  Abstract: A sparse representation is known to be an effective means to encode precise lexical cues in information retrieval tasks by associating each dimension with a unique n-gram-based feature. However, it has often relied on term frequency (such as tf-idf and BM25) or hand-engineered features that are coarse-grained (document-level) and often task-specific, hence not easily generalizable and not appropriate for fine-grained (word or phrase-level) retrieval. In this work, we propose an effective method for learning a highly contextualized, word-level sparse representation by utilizing rectified self-attention weights on the neighboring n-grams. We kernelize the inner product space during training for memory efficiency without the explicit mapping of the large sparse vectors. We particularly focus on the application of our model to phrase retrieval problem, which has recently shown to be a promising direction for open-domain question answering (QA) and requires lexically sensitive phrase encoding. We demonstrate the effectiveness of the learned sparse representations by not only drastically improving the phrase retrieval accuracy (by more than 4%), but also outperforming all other (pipeline-based) open-domain QA methods with up to x97 inference in SQuADopen and CuratedTrec.

Includes members:
  Member "latent retrieval for weakly supervised open domain question answering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "differentiable reasoning over a virtual knowledge base")
  Member "multi passage bert a globally normalized bert model for open domain question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to retrieve reasoning paths over wikipedia graph for question answering")
  Member "rankqa neural question answering with answer re ranking":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "conversations with documents an exploration of document centered assistance")
  Member "ranking paragraphs for improving answer recall in open domain question answering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dc bert decoupling question and document for efficient contextual encoding")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "evidence aggregation for answer re ranking in open domain question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)