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: "flow improving flow based generative models with variational dequantization and architecture design"
  Abstract: Flow-based generative models are powerful exact likelihood models with efficient sampling and inference. Despite their computational efficiency, flow-based models generally have much worse density modeling performance compared to state-of-the-art autoregressive models. In this paper, we investigate and improve upon three limiting design choices employed by flow-based models in prior work: the use of uniform noise for dequantization, the use of inexpressive affine flows, and the use of purely convolutional conditioning networks in coupling layers. Based on our findings, we propose Flow++, a new flow-based model that is now the state-of-the-art non-autoregressive model for unconditional density estimation on standard image benchmarks. Our work has begun to close the significant performance gap that has so far existed between autoregressive models and flow-based models. Our implementation is available at this https URL

Connected through hyperedge HE-1 (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"
    - "emerging convolutions for generative normalizing flows"
    - "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-2 (induced by source paper "exact information bottleneck with invertible neural networks getting the best of discriminative and generative modeling"):
  with members:
    - "conditional generative models are not robust"
    - "hexagan generative adversarial nets for real world classification"
    - "sum of squares polynomial flow"
    - "hybrid models with deep and invertible features"
    - "guided image generation with conditional invertible neural networks"
    - "invertible residual networks"
    - "excessive invariance causes adversarial vulnerability"
    - "mine mutual information neural estimation"

Connected through hyperedge HE-3 (induced by source paper "disentanglement by nonlinear ica with general incompressible flow networks gin"):
  with members:
    - "variational autoencoder with truncated mixture of gaussians for functional connectivity analysis"
    - "guided image generation with conditional invertible neural networks"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "i revnet deep invertible networks"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "emnist an extension of mnist to handwritten letters"
    - "density estimation using real nvp"

Connected through hyperedge HE-4 (induced by source paper "alignflow cycle consistent learning from multiple domains via normalizing flows"):
  with members:
    - "domain adaptation with asymmetrically relaxed distribution alignment"
    - "video to video synthesis"
    - "neural autoregressive flows"
    - "universal neural machine translation for extremely low resource languages"
    - "multimodal unsupervised image to image translation"
    - "neural ordinary differential equations"
    - "generalization bounds for unsupervised cross domain mapping with wgans"
    - "ffjord free form continuous dynamics for scalable reversible generative models"

Connected through hyperedge HE-5 (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"
    - "emerging convolutions for generative normalizing flows"
    - "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-6 (induced by source paper "dimensionality reduction flows"):
  with members:
    - "videoflow a flow based generative model for video"
    - "semi supervised learning with normalizing flows"
    - "emerging convolutions for generative 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-7 (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"
    - "emerging convolutions for generative normalizing flows"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "structured output learning with conditional generative flows"):
  with members:
    - "macow masked convolutional generative flow"
    - "emerging convolutions for generative normalizing flows"
    - "neural autoregressive flows"
    - "deep structured prediction with nonlinear output transformations"
    - "neural ordinary differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "learning approximate inference networks for structured prediction"
    - "generative image inpainting with contextual attention"

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)