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: "softmax q distribution estimation for structured prediction a theoretical interpretation for raml"
  Abstract: Reward augmented maximum likelihood (RAML), a simple and effective learning framework to directly optimize towards the reward function in structured prediction tasks, has led to a number of impressive empirical successes. RAML incorporates task-specific reward by performing maximum-likelihood updates on candidate outputs sampled according to an exponentiated payoff distribution, which gives higher probabilities to candidates that are close to the reference output. While RAML is notable for its simplicity, efficiency, and its impressive empirical successes, the theoretical properties of RAML, especially the behavior of the exponentiated payoff distribution, has not been examined thoroughly. In this work, we introduce softmax Q-distribution estimation, a novel theoretical interpretation of RAML, which reveals the relation between RAML and Bayesian decision theory. The softmax Q-distribution can be regarded as a smooth approximation of the Bayes decision boundary, and the Bayes decision rule is achieved by decoding with this Q-distribution. We further show that RAML is equivalent to approximately estimating the softmax Q-distribution, with the temperature $\tau$ controlling approximation error. We perform two experiments, one on synthetic data of multi-class classification and one on real data of image captioning, to demonstrate the relationship between RAML and the proposed softmax Q-distribution estimation, verifying our theoretical analysis. Additional experiments on three structured prediction tasks with rewards defined on sequential (named entity recognition), tree-based (dependency parsing) and irregular (machine translation) structures show notable improvements over maximum likelihood baselines.

Connected through hyperedge HE-1 (induced by source paper "understanding knowledge distillation in non autoregressive machine translation"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "syntactically supervised transformers for faster neural machine translation"
    - "latent variable non autoregressive neural machine translation with deterministic inference using a delta posterior"
    - "retrieving sequential information for non autoregressive neural machine translation"
    - "mask predict parallel decoding of conditional masked language models"
    - "imitation learning for non autoregressive neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "transfer reward learning for policy gradient based text generation"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning to evaluate image captioning"
    - "actor critic sequence training for image captioning"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "attention correctness in neural image captioning"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "an actor critic algorithm for sequence prediction"
    - "reward augmented maximum likelihood for neural structured prediction"

Connected through hyperedge HE-3 (induced by source paper "optimal completion distillation for sequence learning"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "token level and sequence level loss smoothing for rnn language models"
    - "switchout an efficient data augmentation algorithm for neural machine translation"
    - "convergence of value aggregation for imitation learning"
    - "improved training of end to end attention models for speech recognition"
    - "letter based speech recognition with gated convnets"

Connected through hyperedge HE-4 (induced by source paper "connecting the dots between mle and rl for sequence generation"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "unsupervised text style transfer using language models as discriminators"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "policy shaping and generalized update equations for semantic parsing from denotations"
    - "toward controlled generation of text"
    - "selective encoding for abstractive sentence summarization"
    - "equivalence between policy gradients and soft q learning"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "from credit assignment to entropy regularization two new algorithms for neural sequence prediction"):
  with members:
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "equivalence between policy gradients and soft q learning"
    - "learning to decode for future success"
    - "bridging the gap between value and policy based reinforcement learning"
    - "reinforcement learning with deep energy based policies"
    - "sequence to sequence learning as beam search optimization"
    - "asynchronous methods for deep reinforcement learning"
    - "an actor critic algorithm for sequence prediction"

Connected through hyperedge HE-6 (induced by source paper "approximate distribution matching for sequence to sequence learning"):
  with members:
    - "attention is all you need"
    - "generative bridging network in neural sequence prediction"
    - "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-7 (induced by source paper "switchout an efficient data augmentation algorithm for neural machine translation"):
  with members:
    - "data augmentation for low resource neural machine translation"
    - "attention is all you need"
    - "synthetic and natural noise both break neural machine translation"
    - "improved regularization of convolutional neural networks with cutout"
    - "reward augmented maximum likelihood for neural structured prediction"
    - "edinburgh neural machine translation systems for wmt 16"
    - "effective approaches to attention based neural machine translation"
    - "sequence level training with recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "generative bridging network in neural sequence prediction"):
  with members:
    - "prior knowledge integration for neural machine translation using posterior regularization"
    - "selective encoding for abstractive sentence summarization"
    - "regularizing neural networks by penalizing confident output distributions"
    - "sequence to sequence learning as beam search optimization"
    - "an actor critic algorithm for sequence prediction"
    - "semi supervised learning for neural machine translation"
    - "reward augmented maximum likelihood for neural structured prediction"
    - "improving neural machine translation models with monolingual data"

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)