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 "reqa an evaluation for end to end answer retrieval models"):
  Abstract: Popular QA benchmarks like SQuAD have driven progress on the task of identifying answer spans within a specific passage, with models now surpassing human performance. However, retrieving relevant answers from a huge corpus of documents is still a challenging problem, and places different requirements on the model architecture. There is growing interest in developing scalable answer retrieval models trained end-to-end, bypassing the typical document retrieval step. In this paper, we introduce Retrieval Question Answering (ReQA), a benchmark for evaluating large-scale sentence- and paragraph-level answer retrieval models. We establish baselines using both neural encoding models as well as classical information retrieval techniques. We release our evaluation code to encourage further work on this challenging task.

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 "training neural response selection for task oriented dialogue systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient intent detection with dual sentence encoders")
  Member "improving multilingual sentence embedding using bi directional dual encoder with additive margin softmax":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "absent cross lingual sentence representation mapping with bidirectional gans")
  Member "multilingual universal sentence encoder for semantic retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient intent detection with dual sentence encoders")
  Member "learning semantic textual similarity from conversations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "toward interpretability of dual encoder models for dialogue response suggestions")
  Member "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multilingual denoising pre training for neural machine translation")
  Member "effective parallel corpus mining using bilingual sentence embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "contextual lensing of universal sentence representations")
  Member "end to end retrieval in continuous space":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning contextualized document representations for healthcare answer retrieval")

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)