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 to generate with memory"
  Abstract: Memory units have been widely used to enrich the capabilities of deep networks on capturing long-term dependencies in reasoning and prediction tasks, but little investigation exists on deep generative models (DGMs) which are good at inferring high-level invariant representations from unlabeled data. This paper presents a deep generative model with a possibly large external memory and an attention mechanism to capture the local detail information that is often lost in the bottom-up abstraction process in representation learning. By adopting a smooth attention model, the whole network is trained end-to-end by optimizing a variational bound of data likelihood via auto-encoding variational Bayesian methods, where an asymmetric recognition network is learnt jointly to infer high-level invariant representations. The asymmetric architecture can reduce the competition between bottom-up invariant feature extraction and top-down generation of instance details. Our experiments on several datasets demonstrate that memory can significantly boost the performance of DGMs and even achieve state-of-the-art results on various tasks, including density estimation, image generation, and missing value imputation.

Connected through hyperedge HE-1 (induced by source paper "progressive learning and disentanglement of hierarchical representations"):
  with members:
    - "improving disentangled representation learning with the beta bernoulli process"
    - "isolating sources of disentanglement in variational autoencoders"
    - "a fully progressive approach to single image super resolution"
    - "progressive growing of gans for improved quality stability and variation"
    - "learning hierarchical features from generative models"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "an architecture for deep hierarchical generative models"
    - "deep generative image models using a laplacian pyramid of adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection"):
  with members:
    - "mptv matching pursuit based total variation minimization for image deconvolution"
    - "memorization precedes generation learning unsupervised gans with memory networks"
    - "adversarially learned one class classifier for novelty detection"
    - "deep anomaly detection using geometric transformations"
    - "and autoregressive novelty detectors"
    - "anomaly detection using one class neural networks"
    - "unmasking the abnormal events in video"
    - "joint detection and recounting of abnormal events by learning deep generic knowledge"

Connected through hyperedge HE-3 (induced by source paper "memorization precedes generation learning unsupervised gans with memory networks"):
  with members:
    - "mcgan mean and covariance feature matching gan"
    - "face aging with conditional generative adversarial networks"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "calibrating energy based generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "measuring catastrophic forgetting in neural networks"
    - "semi supervised learning with context conditional generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "generative temporal models with memory"):
  with members:
    - "one shot learning with memory augmented neural networks"
    - "text understanding with the attention sum reader network"
    - "programming with a differentiable forth interpreter"
    - "attend infer repeat fast scene understanding with generative models"
    - "end to end memory networks"
    - "data efficient learning of feedback policies from image pixels using deep dynamical models"
    - "deep residual learning for image recognition"
    - "a recurrent latent variable model for sequential data"

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"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "variational inference for monte carlo objectives"
    - "importance weighted autoencoders"
    - "end to end memory networks"
    - "draw a recurrent neural network for image generation"

Connected through hyperedge HE-6 (induced by source paper "learning hierarchical features from generative models"):
  with members:
    - "improving variational inference with inverse autoregressive flow"
    - "an architecture for deep hierarchical generative models"
    - "conditional image generation with pixelcnn decoders"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "deconstructing the ladder network architecture"
    - "deep unsupervised learning using nonequilibrium thermodynamics"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep generative image models using a laplacian pyramid of adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "max margin deep generative models for semi supervised learning"):
  with members:
    - "improved techniques for training gans"
    - "theano a python framework for fast computation of mathematical expressions"
    - "on the quantitative analysis of decoder based generative models"
    - "importance weighted autoencoders"
    - "learning deep generative models with doubly stochastic mcmc"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)