human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "no metrics are perfect adversarial reward learning for visual storytelling"):
  with members:
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "video captioning via hierarchical reinforcement learning"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "towards an automatic turing test learning to evaluate dialogue responses"
    - "hierarchically attentive rnn for album summarization and storytelling"
    - "multimodal storytelling via generative adversarial imitation learning"
    - "optiongan learning joint reward policy options using generative adversarial inverse reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "approximate distribution matching for sequence to sequence learning"):
  with members:
    - "softmax q distribution estimation for structured prediction a theoretical interpretation for raml"
    - "attention is all you need"
    - "a deep reinforced model for abstractive summarization"
    - "sequence to sequence learning as beam search optimization"
    - "guided alignment training for topic aware neural machine translation"
    - "an actor critic algorithm for sequence prediction"
    - "reward augmented maximum likelihood for neural structured prediction"
    - "self critical sequence training for image captioning"

Connected through hyperedge HE-3 (induced by source paper "enhancing the robustness of prior network in out of distribution detection"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "triangular architecture for rare language translation"
    - "evidential deep learning to quantify classification uncertainty"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "attention is all you need"
    - "regularizing neural networks by penalizing confident output distributions"
    - "aggregated residual transformations for deep neural networks"
    - "guided alignment training for topic aware neural machine translation"

Question: Please tell me which class the center node 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)