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: "sharing deep generative representation for perceived image reconstruction from human brain activity"
  Abstract: Decoding human brain activities via functional magnetic resonance imaging (fMRI) has gained increasing attention in recent years. While encouraging results have been reported in brain states classification tasks, reconstructing the details of human visual experience still remains difficult. Two main challenges that hinder the development of effective models are the perplexing fMRI measurement noise and the high dimensionality of limited data instances. Existing methods generally suffer from one or both of these issues and yield dissatisfactory results. In this paper, we tackle this problem by casting the reconstruction of visual stimulus as the Bayesian inference of missing view in a multiview latent variable model. Sharing a common latent representation, our joint generative model of external stimulus and brain response is not only "deep" in extracting nonlinear features from visual images, but also powerful in capturing correlations among voxel activities of fMRI recordings. The nonlinearity and deep structure endow our model with strong representation ability, while the correlations of voxel activities are critical for suppressing noise and improving prediction. We devise an efficient variational Bayesian method to infer the latent variables and the model parameters. To further improve the reconstruction accuracy, the latent representations of testing instances are enforced to be close to that of their neighbours from the training set via posterior regularization. Experiments on three fMRI recording datasets demonstrate that our approach can more accurately reconstruct visual stimuli.

Connected through hyperedge HE-1 (induced by source paper "shared generative latent representation learning for multi view clustering"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "deep variational canonical correlation analysis"
    - "deep subspace clustering networks"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "unsupervised deep embedding for clustering analysis"
    - "adam a method for stochastic optimization"
    - "a survey on multi view learning"

Connected through hyperedge HE-2 (induced by source paper "reconstructing perceived images from brain activity by visually guided cognitive representation and adversarial learning"):
  with members:
    - "disentangling latent space for vae by label relevant irrelevant dimensions"
    - "improved techniques for training gans"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "correlational neural networks"
    - "autoencoding beyond pixels using a learned similarity metric"

Connected through hyperedge HE-3 (induced by source paper "protecting privacy of users in brain computer interface applications"):
  with members:
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr"

Connected through hyperedge HE-4 (induced by source paper "constraint free natural image reconstruction from fmri signals based on convolutional neural network"):
  with members:
    - "visualizing deep convolutional neural networks using natural pre images"
    - "understanding deep image representations by inverting them"
    - "distributed representations of sentences and documents"
    - "matconvnet convolutional neural networks for matlab"
    - "caffe convolutional architecture for fast feature embedding"
    - "a convolutional neural network for modelling sentences"

Connected through hyperedge HE-5 (induced by source paper "accurate reconstruction of image stimuli from human fmri based on the decoding model with capsule network architecture"):
  with members:
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

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)