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 "generative bridging network in neural sequence prediction"):
  Abstract: Maximum Likelihood Estimation (MLE) suffers from data sparsity problem in sequence prediction tasks where training resource is rare. In order to alleviate this problem, in this paper, we propose a novel generative bridging network (GBN) to train sequence prediction models, which contains a generator and a bridge. Unlike MLE directly maximizing the likelihood of the ground truth, the bridge extends the point-wise ground truth to a bridge distribution (containing inexhaustible examples), and the generator is trained to minimize their KL-divergence. In order to guide the training of generator with additional signals, the bridge distribution can be set or trained to possess specific properties, by using different constraints. More specifically, to increase output diversity, enhance language smoothness and relieve learning burden, three different regularization constraints are introduced to construct bridge distributions. By combining these bridges with a sequence generator, three independent GBNs are proposed, namely uniform GBN, language-model GBN and coaching GBN. Experiment conducted on two recognized sequence prediction tasks (machine translation and abstractive text summarization) shows that our proposed GBNs can yield significant improvements over strong baseline systems. Furthermore, by analyzing samples drawn from bridge distributions, expected influences on the sequence model training are verified.

Includes members:
  Member "prior knowledge integration for neural machine translation using posterior regularization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "glyce glyph vectors for chinese character representations")
  Member "softmax q distribution estimation for structured prediction a theoretical interpretation for raml":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "understanding knowledge distillation in non autoregressive machine translation")
  Member "selective encoding for abstractive sentence summarization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "selective attention encoders by syntactic graph convolutional networks for document summarization")
  Member "regularizing neural networks by penalizing confident output distributions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised domain adaptive object detection using forward backward cyclic adaptation")
  Member "sequence to sequence learning as beam search optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gradient based adversarial attacks on categorical sequence models via traversing an embedded world")
  Member "an actor critic algorithm for sequence prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "semi supervised learning for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bi decoder augmented network for neural machine translation")
  Member "reward augmented maximum likelihood for neural structured prediction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "estimating gradients for discrete random variables by sampling without replacement")

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.AI(Artificial Intelligence)