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 "ask the right questions active question reformulation with reinforcement learning"):
  Abstract: We frame Question Answering (QA) as a Reinforcement Learning task, an approach that we call Active Question Answering. We propose an agent that sits between the user and a black box QA system and learns to reformulate questions to elicit the best possible answers. The agent probes the system with, potentially many, natural language reformulations of an initial question and aggregates the returned evidence to yield the best answer. The reformulation system is trained end-to-end to maximize answer quality using policy gradient. We evaluate on SearchQA, a dataset of complex questions extracted from Jeopardy!. The agent outperforms a state-of-the-art base model, playing the role of the environment, and other benchmarks. We also analyze the language that the agent has learned while interacting with the question answering system. We find that successful question reformulations look quite different from natural language paraphrases. The agent is able to discover non-trivial reformulation strategies that resemble classic information retrieval techniques such as term re-weighting (tf-idf) and stemming.

Includes members:
  Member "massive exploration of neural machine translation architectures":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "meta embeddings based on self attention")
  Member "deal or no deal end to end learning for negotiation dialogues":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "recent advances and challenges in task oriented dialog system")
  Member "task oriented query reformulation with reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised question decomposition for question answering")
  Member "adversarial examples for evaluating reading comprehension systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "deep reinforcement learning for dialogue generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "neural symbolic machines learning semantic parsers on freebase with weak supervision":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "recent advances and challenges in task oriented dialog system")
  Member "squad 100 000 questions for machine comprehension of text":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "end to end goal driven web navigation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "navigation based candidate expansion and pretrained language models for citation recommendation")

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)