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: "variational memory addressing in generative models"
  Abstract: Aiming to augment generative models with external memory, we interpret the output of a memory module with stochastic addressing as a conditional mixture distribution, where a read operation corresponds to sampling a discrete memory address and retrieving the corresponding content from memory. This perspective allows us to apply variational inference to memory addressing, which enables effective training of the memory module by using the target information to guide memory lookups. Stochastic addressing is particularly well-suited for generative models as it naturally encourages multimodality which is a prominent aspect of most high-dimensional datasets. Treating the chosen address as a latent variable also allows us to quantify the amount of information gained with a memory lookup and measure the contribution of the memory module to the generative process. To illustrate the advantages of this approach we incorporate it into a variational autoencoder and apply the resulting model to the task of generative few-shot learning. The intuition behind this architecture is that the memory module can pick a relevant template from memory and the continuous part of the model can concentrate on modeling remaining variations. We demonstrate empirically that our model is able to identify and access the relevant memory contents even with hundreds of unseen Omniglot characters in memory

Connected through hyperedge HE-1 (induced by source paper "hierarchical quantized autoencoders"):
  with members:
    - "exact rate distortion in autoencoders via echo noise"
    - "on the variance of the adaptive learning rate and beyond"
    - "hierarchical autoregressive image models with auxiliary decoders"
    - "rethinking lossy compression the rate distortion perception tradeoff"
    - "the perception distortion tradeoff"
    - "deep generative models for distribution preserving lossy compression"
    - "recent advances in autoencoder based representation learning"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "dawson a domain adaptive few shot generation framework"):
  with members:
    - "metalgan multi domain label less image synthesis using cgans and meta learning"
    - "figr few shot image generation with reptile"
    - "counterpoint by convolution"
    - "this time with feeling learning expressive musical performance"
    - "convolutional generative adversarial networks with binary neurons for polyphonic music generation"
    - "sample efficient adaptive text to speech"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "online learning with gated linear networks"

Connected through hyperedge HE-3 (induced by source paper "meta amortized variational inference and learning"):
  with members:
    - "attentive neural processes"
    - "the variational homoencoder learning to learn high capacity generative models from few examples"
    - "understanding and improving interpolation in autoencoders via an adversarial regularizer"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "bayesian model agnostic meta learning"
    - "large scale gan training for high fidelity natural image synthesis"
    - "prototypical networks for few shot learning"
    - "few shot autoregressive density estimation towards learning to learn distributions"

Connected through hyperedge HE-4 (induced by source paper "attentive neural processes"):
  with members:
    - "the variational homoencoder learning to learn high capacity generative models from few examples"
    - "image transformer"
    - "consistent generative query networks"
    - "conditional neural processes"
    - "vfunc a deep generative model for functions"
    - "learning models for visual 3d localization with implicit mapping"
    - "attention is all you need"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-5 (induced by source paper "generative hierarchical models for parts objects and scenes"):
  with members:
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "cerberus a multi headed derenderer"
    - "3d aware scene manipulation via inverse graphics"
    - "dynamic routing between capsules"
    - "an architecture for deep hierarchical generative models"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "attend infer repeat fast scene understanding with generative models"

Connected through hyperedge HE-6 (induced by source paper "sample efficient adaptive text to speech"):
  with members:
    - "neural voice cloning with a few samples"
    - "playing hard exploration games by watching youtube"
    - "transfer learning from speaker verification to multispeaker text to speech synthesis"
    - "observe and look further achieving consistent performance on atari"
    - "fitting new speakers based on a short untranscribed sample"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "towards end to end prosody transfer for expressive speech synthesis with tacotron"
    - "voiceloop voice fitting and synthesis via a phonological loop"

Connected through hyperedge HE-7 (induced by source paper "conditional neural processes"):
  with members:
    - "the variational homoencoder learning to learn high capacity generative models from few examples"
    - "neural program meta induction"
    - "prototypical networks for few shot learning"
    - "few shot autoregressive density estimation towards learning to learn distributions"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to reinforcement learn"
    - "one shot learning with memory augmented neural networks"
    - "conditional image generation with pixelcnn decoders"

Connected through hyperedge HE-8 (induced by source paper "consistent generative query networks"):
  with members:
    - "learning and querying fast generative models for reinforcement learning"
    - "stochastic variational video prediction"
    - "few shot autoregressive density estimation towards learning to learn distributions"
    - "video frame synthesis using deep voxel flow"
    - "unsupervised learning for physical interaction through video prediction"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "matching networks for one shot learning"

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)