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: "integer discrete flows and lossless compression"
  Abstract: Lossless compression methods shorten the expected representation size of data without loss of information, using a statistical model. Flow-based models are attractive in this setting because they admit exact likelihood optimization, which is equivalent to minimizing the expected number of bits per message. However, conventional flows assume continuous data, which may lead to reconstruction errors when quantized for compression. For that reason, we introduce a flow-based generative model for ordinal discrete data called Integer Discrete Flow (IDF): a bijective integer map that can learn rich transformations on high-dimensional data. As building blocks for IDFs, we introduce a flexible transformation layer called integer discrete coupling. Our experiments show that IDFs are competitive with other flow-based generative models. Furthermore, we demonstrate that IDF based compression achieves state-of-the-art lossless compression rates on CIFAR10, ImageNet32, and ImageNet64. To the best of our knowledge, this is the first lossless compression method that uses invertible neural networks.

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"
    - "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"
    - "discrete flows invertible generative models of discrete data"
    - "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:
    - "discrete flows invertible generative models of discrete data"
    - "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"
    - "sum of squares polynomial flow"
    - "discrete flows invertible generative models of discrete data"
    - "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:
    - "discrete flows invertible generative models of discrete data"
    - "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 "learning likelihoods with conditional normalizing flows"):
  with members:
    - "emerging convolutions for generative normalizing flows"
    - "guided image generation with conditional invertible neural networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "learning to maintain natural image statistics"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "a downsampled variant of imagenet as an alternative to the cifar datasets"

Connected through hyperedge HE-6 (induced by source paper "discrete flows invertible generative models of discrete data"):
  with members:
    - "waveglow a flow based generative network for speech synthesis"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "blockwise parallel decoding for deep autoregressive models"
    - "bayesian layers a module for neural network uncertainty"
    - "an analysis of neural language modeling at multiple scales"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "the importance of generation order in language modeling"
    - "attention is all you need"

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)