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: "optimal transport maps for distribution preserving operations on latent spaces of generative models"
  Abstract: Generative models such as Variational Auto Encoders (VAEs) and Generative Adversarial Networks (GANs) are typically trained for a fixed prior distribution in the latent space, such as uniform or Gaussian. After a trained model is obtained, one can sample the Generator in various forms for exploration and understanding, such as interpolating between two samples, sampling in the vicinity of a sample or exploring differences between a pair of samples applied to a third sample. In this paper, we show that the latent space operations used in the literature so far induce a distribution mismatch between the resulting outputs and the prior distribution the model was trained on. To address this, we propose to use distribution matching transport maps to ensure that such latent space operations preserve the prior distribution, while minimally modifying the original operation. Our experimental results validate that the proposed operations give higher quality samples compared to the original operations.

Connected through hyperedge HE-1 (induced by source paper "uniform interpolation constrained geodesic learning on data manifold"):
  with members:
    - "understanding and improving interpolation in autoencoders via an adversarial regularizer"
    - "generative adversarial interpolative autoencoding adversarial training on latent space interpolations encourages convex latent distributions"
    - "dimal deep isometric manifold learning using sparse geodesic sampling"
    - "geodesic clustering in deep generative models"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-2 (induced by source paper "non parametric priors for generative adversarial networks"):
  with members:
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "exploring disentangled feature representation beyond face identification"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "sampling generative networks"
    - "adversarial discriminative domain adaptation"
    - "semantic interpolation in implicit models"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "interpolation in generative models"):
  with members:
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "sampling generative networks"
    - "semantic interpolation in implicit models"
    - "neural photo editing with introspective adversarial networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "generating sentences from a continuous space"
    - "autoencoding beyond pixels using a learned similarity metric"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-4 (induced by source paper "realism index interpolation in generative models with arbitrary prior"):
  with members:
    - "semantic interpolation in implicit models"
    - "neural photo editing with introspective adversarial networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "generating sentences from a continuous space"
    - "autoencoding beyond pixels using a learned similarity metric"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-5 (induced by source paper "on latent distributions without finite mean in generative models"):
  with members:
    - "sampling generative networks"
    - "semantic interpolation in implicit models"
    - "adversarial feature learning"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep learning face attributes in the wild"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "logo synthesis and manipulation with clustered generative adversarial networks"):
  with members:
    - "calibrating energy based generative adversarial networks"
    - "improved training of wasserstein gans"
    - "invertible conditional gans for image editing"
    - "neural photo editing with introspective adversarial networks"
    - "nips 2016 tutorial generative adversarial networks"
    - "stacked generative adversarial networks"
    - "conditional image generation with pixelcnn decoders"
    - "improved techniques for training 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)