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: "discrete autoencoders for sequence models"
  Abstract: Recurrent models for sequences have been recently successful at many tasks, especially for language modeling and machine translation. Nevertheless, it remains challenging to extract good representations from these models. For instance, even though language has a clear hierarchical structure going from characters through words to sentences, it is not apparent in current language models. We propose to improve the representation in sequence models by augmenting current approaches with an autoencoder that is forced to compress the sequence through an intermediate discrete latent space. In order to propagate gradients though this discrete representation we introduce an improved semantic hashing technique. We show that this technique performs well on a newly proposed quantitative efficiency measure. We also analyze latent codes produced by the model showing how they correspond to words and phrases. Finally, we present an application of the autoencoder-augmented model to generating diverse translations.

Connected through hyperedge HE-1 (induced by source paper "model based reinforcement learning for atari"):
  with members:
    - "learning powerful policies by using consistent dynamics model"
    - "when to use parametric models in reinforcement learning"
    - "benchmarking model based reinforcement learning"
    - "dopamine a research framework for deep reinforcement learning"
    - "learning real world robot policies by dreaming"
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "model based regularization for deep reinforcement learning with transcoder networks"

Connected through hyperedge HE-2 (induced by source paper "self adaptive network pruning"):
  with members:
    - "autopruner an end to end trainable filter pruning method for efficient deep model inference"
    - "channel gating neural networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "dynamic channel pruning feature boosting and suppression"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"

Connected through hyperedge HE-3 (induced by source paper "encoding musical style with transformer autoencoders"):
  with members:
    - "exploring conditioning for generative music systems with human interpretable controls"
    - "learning to groove with inverse sequence transformations"
    - "improving automatic jazz melody generation by transfer learning techniques"
    - "counterpoint by convolution"
    - "this time with feeling learning expressive musical performance"
    - "enabling factorized piano music modeling and generation with the maestro dataset"
    - "self attention with relative position representations"
    - "a hierarchical latent vector model for learning long term structure in music"

Connected through hyperedge HE-4 (induced by source paper "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"):
  with members:
    - "the state of sparsity in deep neural networks"
    - "training tips for the transformer model"
    - "context aware neural machine translation learns anaphora resolution"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "the importance of being recurrent for modeling hierarchical structure"
    - "an analysis of attention mechanisms the case of word sense disambiguation in neural machine translation"
    - "attention is all you need"
    - "what does attention in neural machine translation pay attention to"

Connected through hyperedge HE-5 (induced by source paper "extractive summary as discrete latent variables"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "achieving human parity on automatic chinese to english news translation"
    - "on accurate evaluation of gans for language generation"
    - "generating wikipedia by summarizing long sequences"
    - "hierarchical text generation using an outline"
    - "deep contextualized word representations"
    - "hierarchical neural story generation"

Connected through hyperedge HE-6 (induced by source paper "neural joint source channel coding"):
  with members:
    - "deepcode feedback codes via deep learning"
    - "deep joint source channel coding for wireless image transmission"
    - "theory and experiments on vector quantized autoencoders"
    - "deep learning for joint source channel coding of text"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "infovae information maximizing variational autoencoders"
    - "scaling deep learning based decoding of polar codes via partitioning"

Connected through hyperedge HE-7 (induced by source paper "fast decoding in sequence models using discrete latent variables"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "learning the base distribution in implicit generative models"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "attention is all you need"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "convolutional sequence to sequence learning"
    - "compressing word embeddings via deep compositional code learning"

Connected through hyperedge HE-8 (induced by source paper "you look twice gaternet for dynamic filter selection in cnns"):
  with members:
    - "learning sparse deep feedforward networks via tree skeleton expansion"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "learning transferable architectures for scalable image recognition"
    - "convolutional networks with adaptive inference graphs"
    - "improved regularization of convolutional neural networks with cutout"
    - "shake shake regularization"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"

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)