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: "discrete flows invertible generative models of discrete data"
  Abstract: While normalizing flows have led to significant advances in modeling high-dimensional continuous distributions, their applicability to discrete distributions remains unknown. In this paper, we show that flows can in fact be extended to discrete events---and under a simple change-of-variables formula not requiring log-determinant-Jacobian computations. Discrete flows have numerous applications. We consider two flow architectures: discrete autoregressive flows that enable bidirectionality, allowing, for example, tokens in text to depend on both left-to-right and right-to-left contexts in an exact language model; and discrete bipartite flows that enable efficient non-autoregressive generation as in RealNVP. Empirically, we find that discrete autoregressive flows outperform autoregressive baselines on synthetic discrete distributions, an addition task, and Potts models; and bipartite flows can obtain competitive performance with autoregressive baselines on character-level language modeling for Penn Tree Bank and text8.

Connected through hyperedge HE-1 (induced by source paper "on the discrepancy between density estimation and sequence generation"):
  with members:
    - "towards a human like open domain chatbot"
    - "latent variable non autoregressive neural machine translation with deterministic inference using a delta posterior"
    - "integer discrete flows and lossless compression"
    - "understanding knowledge distillation in non autoregressive machine translation"
    - "molecular geometry prediction using a deep generative graph neural network"
    - "mixture models for diverse machine translation tricks of the trade"
    - "flowseq non autoregressive conditional sequence generation with generative flow"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "woodbury transformations for deep generative flows"):
  with members:
    - "integer discrete flows and lossless compression"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "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-3 (induced by source paper "closing the dequantization gap pixelcnn as a single layer flow"):
  with members:
    - "learning discrete distributions by dequantization"
    - "integer discrete flows and lossless compression"
    - "sum of squares polynomial flow"
    - "generating long sequences with sparse transformers"
    - "image transformer"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"

Connected through hyperedge HE-4 (induced by source paper "learning discrete distributions by dequantization"):
  with members:
    - "integer discrete flows and lossless compression"
    - "mintnet building invertible neural networks with masked convolutions"
    - "emerging convolutions for generative normalizing flows"
    - "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 "self supervised learning of generative spin glasses with normalizing flows"):
  with members:
    - "multi task self supervised visual learning"
    - "density estimation using real nvp"

Connected through hyperedge HE-6 (induced by source paper "style example guided text generation using generative adversarial transformers"):
  with members:
    - "insertion transformer flexible sequence generation via insertion operations"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "real or fake learning to discriminate machine from human generated text"
    - "ctrl a conditional transformer language model for controllable generation"
    - "pretraining based natural language generation for text summarization"
    - "multi task deep neural networks for natural language understanding"
    - "domain adaptive text style transfer"
    - "defending against neural fake news"

Connected through hyperedge HE-7 (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"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "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-8 (induced by source paper "integer discrete flows and lossless compression"):
  with members:
    - "practical lossless compression with latent variables using bits back coding"
    - "videoflow a flow based generative model for video"
    - "emerging convolutions for generative normalizing flows"
    - "bit swap recursive bits back coding for lossless compression with hierarchical latent variables"
    - "waveglow a flow based generative network for speech synthesis"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "density estimation using real nvp"

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)