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: "mintnet building invertible neural networks with masked convolutions"
  Abstract: We propose a new way of constructing invertible neural networks by combining simple building blocks with a novel set of composition rules. This leads to a rich set of invertible architectures, including those similar to ResNets. Inversion is achieved with a locally convergent iterative procedure that is parallelizable and very fast in practice. Additionally, the determinant of the Jacobian can be computed analytically and efficiently, enabling their generative use as flow models. To demonstrate their flexibility, we show that our invertible neural networks are competitive with ResNets on MNIST and CIFAR-10 classification. When trained as generative models, our invertible networks achieve new state-of-the-art likelihoods on MNIST, CIFAR-10 and ImageNet 32x32, with bits per dimension of 0.98, 3.32 and 4.06 respectively.

Connected through hyperedge HE-1 (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"
    - "hybrid models with deep and invertible features"
    - "emerging convolutions for generative normalizing flows"
    - "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-2 (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"
    - "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-3 (induced by source paper "semi supervised learning with normalizing flows"):
  with members:
    - "unsupervised data augmentation for consistency training"
    - "remixmatch semi supervised learning with distribution alignment and augmentation anchoring"
    - "hybrid models with deep and invertible features"
    - "mixmatch a holistic approach to semi supervised learning"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "invertible residual networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "realistic evaluation of deep semi supervised learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "graph residual flow for molecular graph generation"):
  with members:
    - "graph normalizing flows"
    - "on asymptotic behaviors of graph cnns from dynamical systems perspective"
    - "simplifying graph convolutional networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "constrained graph variational autoencoders for molecule design"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"
    - "spectral normalization for generative adversarial networks"
    - "automatic chemical design using a data driven continuous representation of molecules"

Connected through hyperedge HE-5 (induced by source paper "fis gan gan with flow based importance sampling"):
  with members:
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "neural autoregressive flows"
    - "spectral normalization for generative adversarial networks"
    - "not all samples are created equal deep learning with importance sampling"
    - "invertible residual networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the numerics of gans"
    - "improved training of wasserstein gans"

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)