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: "guided image generation with conditional invertible neural networks"
  Abstract: In this work, we address the task of natural image generation guided by a conditioning input. We introduce a new architecture called conditional invertible neural network (cINN). The cINN combines the purely generative INN model with an unconstrained feed-forward network, which efficiently preprocesses the conditioning input into useful features. All parameters of the cINN are jointly optimized with a stable, maximum likelihood-based training procedure. By construction, the cINN does not experience mode collapse and generates diverse samples, in contrast to e.g. cGANs. At the same time our model produces sharp images since no reconstruction loss is required, in contrast to e.g. VAEs. We demonstrate these properties for the tasks of MNIST digit generation and image colorization. Furthermore, we take advantage of our bi-directional cINN architecture to explore and manipulate emergent properties of the latent space, such as changing the image style in an intuitive way.

Connected through hyperedge HE-1 (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"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "hybrid models with deep and invertible features"
    - "invertible residual networks"
    - "excessive invariance causes adversarial vulnerability"
    - "mine mutual information neural estimation"

Connected through hyperedge HE-2 (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"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "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-3 (induced by source paper "learning likelihoods with conditional normalizing flows"):
  with members:
    - "integer discrete flows and lossless compression"
    - "emerging convolutions for generative normalizing flows"
    - "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-4 (induced by source paper "a method to model conditional distributions with normalizing flows"):
  with members:
    - "generative latent flow a framework for non adversarial image generation"
    - "generating diverse high fidelity images with vq vae 2"
    - "semantic image synthesis with spatially adaptive normalization"
    - "analyzing inverse problems with invertible neural networks"
    - "invertible residual networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "comparison of maximum likelihood and gan based training of real nvps"
    - "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.CV(Computer Vision and Pattern Recognition)