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 "automated curriculum learning for turn level spoken language understanding with weak supervision"):
  Abstract: We propose a learning approach for turn-level spoken language understanding, which facilitates a user to speak one or more utterances compositionally in a turn for completing a task (e.g., voice ordering). A typical pipelined approach for these understanding tasks requires non-trivial annotation effort for developing its multiple components. Also, the pipeline is difficult to port to a new domain or scale up. To address these problems, we propose an end-to-end statistical model with weak supervision. We employ randomized beam search with memory augmentation (RBSMA) to solve complicated problems for which long promising trajectories are usually difficult to explore. Furthermore, considering the diversity of problem complexity, we explore automated curriculum learning (CL) for weak supervision to accelerate exploration and learning. We evaluate the proposed approach on real-world user logs of a commercial voice ordering system. Results demonstrate that when trained on a small number of end-to-end annotated sessions collected with low cost, our model performs comparably to the deployed pipelined system, saving the development labor over an order of magnitude. The RBSMA algorithm improves the test set accuracy by 7.8% relative compared to the standard beam search. Automated CL leads to better generalization and further improves the test set accuracy by 5% relative.

Includes members:
  Member "learning to map context dependent sentences to executable formal queries":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "how far are we from effective context modeling an exploratory study on semantic parsing in context")
  Member "situated mapping of sequential instructions to actions with single step reward observation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "editing based sql query generation for cross domain context dependent questions")
  Member "memory augmented policy optimization for program synthesis and semantic parsing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "automated curriculum learning for neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits")
  Member "from language to programs bridging reinforcement learning and maximum marginal likelihood":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "why build an assistant in minecraft")
  Member "weakly supervised semantic parsing with abstract examples":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning semantic parsers from denotations with latent structured alignments and abstract programs")
  Member "get to the point summarization with pointer generator networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "long text generation via adversarial training with leaked information":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "robust explanations for visual 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)