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: "emerging convolutions for generative normalizing flows"
  Abstract: Generative flows are attractive because they admit exact likelihood optimization and efficient image synthesis. Recently, Kingma & Dhariwal (2018) demonstrated with Glow that generative flows are capable of generating high quality images. We generalize the 1 x 1 convolutions proposed in Glow to invertible d x d convolutions, which are more flexible since they operate on both channel and spatial axes. We propose two methods to produce invertible convolutions that have receptive fields identical to standard convolutions: Emerging convolutions are obtained by chaining specific autoregressive convolutions, and periodic convolutions are decoupled in the frequency domain. Our experiments show that the flexibility of d x d convolutions significantly improves the performance of generative flow models on galaxy images, CIFAR10 and ImageNet.

Connected through hyperedge HE-1 (induced by source paper "relational state space model for stochastic multi object systems"):
  with members:
    - "stochastic prediction of multi agent interactions from partial observations"
    - "graph normalizing flows"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "state space lstm models with particle mcmc inference"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-2 (induced by source paper "augmented normalizing flows bridging the gap between generative flows and latent variable models"):
  with members:
    - "set flow a permutation invariant normalizing flow"
    - "mintnet building invertible neural networks with masked convolutions"
    - "hybrid models with deep and invertible features"
    - "improving exploration in soft actor critic with normalizing flows policies"
    - "leveraging exploration in off policy algorithms via normalizing flows"
    - "stochastic neural network with kronecker flow"
    - "invertible residual networks"
    - "autoregressive quantile networks for generative modeling"

Connected through hyperedge HE-3 (induced by source paper "woodbury transformations for deep generative flows"):
  with members:
    - "integer discrete flows and lossless compression"
    - "discrete flows invertible generative models of discrete data"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "neural autoregressive flows"
    - "neural ordinary differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "progressive growing of gans for improved quality stability and variation"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "learning discrete distributions by dequantization"):
  with members:
    - "integer discrete flows and lossless compression"
    - "discrete flows invertible generative models of discrete data"
    - "mintnet building invertible neural networks with masked convolutions"
    - "learning likelihoods with conditional normalizing flows"
    - "importance weighting and variational inference"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "density estimation using real nvp"
    - "pixel recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "a linear systems theory of normalizing flows"):
  with members:
    - "hybrid models with deep and invertible features"
    - "loss landscapes of regularized linear autoencoders"
    - "density estimation using real nvp"
    - "nice non linear independent components estimation"

Connected through hyperedge HE-6 (induced by source paper "waveflow a compact flow based model for raw audio"):
  with members:
    - "fastspeech fast robust and controllable text to speech"
    - "parallel neural text to speech"
    - "high fidelity speech synthesis with adversarial networks"
    - "discrete flows invertible generative models of discrete data"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "blow a single scale hyperconditioned flow for non parallel raw audio voice conversion"
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "waveglow a flow based generative network for speech synthesis"

Connected through hyperedge HE-7 (induced by source paper "dimensionality reduction flows"):
  with members:
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "videoflow a flow based generative model for video"
    - "semi supervised learning with normalizing flows"
    - "gansynth adversarial neural audio synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "pixelsnail an improved autoregressive generative model"
    - "neural discrete representation learning"

Connected through hyperedge HE-8 (induced by source paper "blow a single scale hyperconditioned flow for non parallel raw audio voice conversion"):
  with members:
    - "unsupervised singing voice conversion"
    - "a rad approach to deep mixture models"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "stargan vc non parallel many to many voice conversion with star generative adversarial networks"
    - "waveglow a flow based generative network for speech synthesis"
    - "conditional recurrent flow conditional generation of longitudinal samples with applications to neuroimaging"
    - "neural voice cloning with a few samples"
    - "efficient neural audio synthesis"

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)