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: "learning wake sleep recurrent attention models"
  Abstract: Despite their success, convolutional neural networks are computationally expensive because they must examine all image locations. Stochastic attention-based models have been shown to improve computational efficiency at test time, but they remain difficult to train because of intractable posterior inference and high variance in the stochastic gradient estimates. Borrowing techniques from the literature on training deep generative models, we present the Wake-Sleep Recurrent Attention Model, a method for training stochastic attention networks which improves posterior inference and which reduces the variability in the stochastic gradients. We show that our method can greatly speed up the training time for stochastic attention networks in the domains of image classification and caption generation.

Connected through hyperedge HE-1 (induced by source paper "doubly reparameterized gradient estimators for monte carlo objectives"):
  with members:
    - "implicit reparameterization gradients"
    - "stochastic video generation with a learned prior"
    - "online learning rate adaptation with hypergradient descent"
    - "stochastic variational video prediction"
    - "stochastic backpropagation through mixture density distributions"
    - "pixelvae a latent variable model for natural images"
    - "variational inference for monte carlo objectives"
    - "importance weighted autoencoders"

Connected through hyperedge HE-2 (induced by source paper "regularizing deep neural networks by noise its interpretation and optimization"):
  with members:
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "improved dropout for shallow and deep learning"
    - "dropout with expectation linear regularization"
    - "convolutional two stream network fusion for video action recognition"
    - "variational inference for monte carlo objectives"
    - "wide residual networks"
    - "fractalnet ultra deep neural networks without residuals"

Connected through hyperedge HE-3 (induced by source paper "saliency based sequential image attention with multiset prediction"):
  with members:
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "feudal networks for hierarchical reinforcement learning"
    - "paying more attention to saliency image captioning with saliency and context attention"
    - "towards instance segmentation with object priority prominent object detection and recognition"
    - "hierarchical attention networks"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "localizing by describing attribute guided attention localization for fine grained recognition"
    - "generating visual explanations"

Connected through hyperedge HE-4 (induced by source paper "learning hard alignments with variational inference"):
  with members:
    - "online and linear time attention by enforcing monotonic alignments"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "learning to translate in real time with neural machine translation"
    - "learning online alignments with continuous rewards policy gradient"
    - "variational inference for monte carlo objectives"
    - "hierarchical multiscale recurrent neural networks"
    - "importance weighted autoencoders"
    - "attention based models for speech recognition"

Connected through hyperedge HE-5 (induced by source paper "variational memory addressing in generative models"):
  with members:
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "generative temporal models with memory"
    - "improving variational inference with inverse autoregressive flow"
    - "learning to generate with memory"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "variational inference for monte carlo objectives"
    - "importance weighted autoencoders"
    - "end to end memory networks"

Connected through hyperedge HE-6 (induced by source paper "textual entailment with structured attentions and composition"):
  with members:
    - "long short term memory networks for machine reading"
    - "incorporating structural alignment biases into an attentional neural translation model"
    - "show attend and tell neural image caption generation with visual attention"
    - "improved semantic representations from tree structured long short term memory networks"
    - "learning natural language inference with lstm"
    - "reasoning about entailment with neural attention"
    - "a large annotated corpus for learning natural language inference"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "why pay more when you can pay less a joint learning framework for active feature acquisition and classification"):
  with members:
    - "recurrent neural networks for multivariate time series with missing values"
    - "deep residual learning for image recognition"
    - "recurrent models of visual attention"
    - "neural machine translation by jointly learning to align and translate"
    - "multiple object recognition with visual attention"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "adaptive feature abstraction for translating video to text"):
  with members:
    - "video captioning with guidance of multimodal latent topics"
    - "multi task video captioning with video and entailment generation"
    - "semantic compositional networks for visual captioning"
    - "frame and segment level features and candidate pool evaluation for video caption generation"
    - "variational inference for monte carlo objectives"
    - "jointly modeling embedding and translation to bridge video and language"
    - "importance weighted autoencoders"
    - "delving deeper into convolutional networks for learning video representations"

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)