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: "reward augmented maximum likelihood for neural structured prediction"
  Abstract: A key problem in structured output prediction is direct optimization of the task reward function that matters for test evaluation. This paper presents a simple and computationally efficient approach to incorporate task reward into a maximum likelihood framework. By establishing a link between the log-likelihood and expected reward objectives, we show that an optimal regularized expected reward is achieved when the conditional distribution of the outputs given the inputs is proportional to their exponentiated scaled rewards. Accordingly, we present a framework to smooth the predictive probability of the outputs using their corresponding rewards. We optimize the conditional log-probability of augmented outputs that are sampled proportionally to their exponentiated scaled rewards. Experiments on neural sequence to sequence models for speech recognition and machine translation show notable improvements over a maximum likelihood baseline by using reward augmented maximum likelihood (RAML), where the rewards are defined as the negative edit distance between the outputs and the ground truth labels.

Connected through hyperedge HE-1 (induced by source paper "estimating gradients for discrete random variables by sampling without replacement"):
  with members:
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "direct policy gradients direct optimization of policies in discrete action spaces"
    - "direct optimization through arg max for discrete variational auto encoder"
    - "memory augmented policy optimization for program synthesis and semantic parsing"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "neural machine translation with gumbel greedy decoding"
    - "classical structured prediction losses for sequence to sequence learning"

Connected through hyperedge HE-2 (induced by source paper "improving molecular design by stochastic iterative target augmentation"):
  with members:
    - "infograph unsupervised and semi supervised graph level representation learning via mutual information maximization"
    - "conditional molecular design with deep generative models"
    - "pre training graph neural networks"
    - "unsupervised data augmentation for consistency training"
    - "mixmatch a holistic approach to semi supervised learning"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "deep reinforcement learning for de novo drug design"
    - "design by adaptive sampling"

Connected through hyperedge HE-3 (induced by source paper "investigating the decoders of maximum likelihood sequence models a look ahead approach"):
  with members:
    - "on nmt search errors and model errors cat got your tongue"
    - "end to end dense video captioning with masked transformer"
    - "a call for clarity in reporting bleu scores"
    - "tvqa localized compositional video question answering"
    - "optimal completion distillation for sequence learning"
    - "attention is all you need"
    - "adversarial learning for neural dialogue generation"
    - "towards better decoding and language model integration in sequence to sequence models"

Connected through hyperedge HE-4 (induced by source paper "introduction to neural network based approaches for question answering over knowledge graphs"):
  with members:
    - "pretrained transformers for simple question answering over knowledge graphs"
    - "learning to generalize from sparse and underspecified rewards"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "practical semantic parsing for spoken language understanding"
    - "question generation from sql queries improves neural semantic parsing"
    - "towards a question answering system over the semantic web"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "policy shaping and generalized update equations for semantic parsing from denotations"

Connected through hyperedge HE-5 (induced by source paper "reinforcement learning based graph to sequence model for natural question generation"):
  with members:
    - "bidirectional attentive memory networks for question answering over knowledge bases"
    - "learning to generate questions by learningwhat not to generate"
    - "graphflow exploiting conversation flow with graph neural networks for conversational machine comprehension"
    - "reinforcement learning based text style transfer without parallel training corpus"
    - "deep iterative and adaptive learning for graph neural networks"
    - "unified language model pre training for natural language understanding and generation"
    - "sql to text generation with graph to sequence model"
    - "towards a better metric for evaluating question generation systems"

Connected through hyperedge HE-6 (induced by source paper "learning to generalize from sparse and underspecified rewards"):
  with members:
    - "self supervised generalisation with meta auxiliary learning"
    - "reward shaping via meta learning"
    - "on first order meta learning algorithms"
    - "multi task maximum entropy inverse reinforcement learning"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "discourse aware neural rewards for coherent text generation"
    - "babyai first steps towards grounded language learning with a human in the loop"

Connected through hyperedge HE-7 (induced by source paper "generalization in generation a closer look at exposure bias"):
  with members:
    - "deep reinforcement learning with distributional semantic rewards for abstractive summarization"
    - "quantifying exposure bias for neural language generation"
    - "sample efficient text summarization using a single pre trained transformer"
    - "autoregressive text generation beyond feedback loops"
    - "deep reinforcement learning for sequence to sequence models"
    - "neural text generation past present and beyond"
    - "learning actionable representations with goal conditioned policies"
    - "isolating sources of disentanglement in variational autoencoders"

Connected through hyperedge HE-8 (induced by source paper "araml a stable adversarial training framework for text generation"):
  with members:
    - "incorporating discriminator in sentence generation a gibbs sampling method"
    - "on accurate evaluation of gans for language generation"
    - "towards diverse text generation with inverse reinforcement learning"
    - "texygen a benchmarking platform for text generation models"
    - "cgmh constrained sentence generation by metropolis hastings sampling"
    - "language gans falling short"
    - "the price of debiasing automatic metrics in natural language evaluation"
    - "emotional chatting machine emotional conversation generation with internal and external memory"

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.LG(Machine Learning)