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: "regularized discrete optimal transport"
  Abstract: This article introduces a generalization of the discrete optimal transport, with applications to color image manipulations. This new formulation includes a relaxation of the mass conservation constraint and a regularization term. These two features are crucial for image processing tasks, which necessitate to take into account families of multimodal histograms, with large mass variation across modes. #R##N#The corresponding relaxed and regularized transportation problem is the solution of a convex optimization problem. Depending on the regularization used, this minimization can be solved using standard linear programming methods or first order proximal splitting schemes. #R##N#The resulting transportation plan can be used as a color transfer map, which is robust to mass variation across images color palettes. Furthermore, the regularization of the transport plan helps to remove colorization artifacts due to noise amplification. #R##N#We also extend this framework to the computation of barycenters of distributions. The barycenter is the solution of an optimization problem, which is separately convex with respect to the barycenter and the transportation plans, but not jointly convex. A block coordinate descent scheme converges to a stationary point of the energy. We show that the resulting algorithm can be used for color normalization across several images. The relaxed and regularized barycenter defines a common color palette for those images. Applying color transfer toward this average palette performs a color normalization of the input images.

Connected through hyperedge HE-1 (induced by source paper "large scale optimal transport via adversarial training with cycle consistency"):
  with members:
    - "on scalable and efficient computation of large scale optimal transport"
    - "adversarial computation of optimal transport maps"
    - "optimal transport mapping via input convex neural networks"
    - "scalable unbalanced optimal transport using generative adversarial networks"
    - "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"
    - "guiding the one to one mapping in cyclegan via optimal transport"
    - "cycada cycle consistent adversarial domain adaptation"
    - "learning to discover cross domain relations with generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "regularized wasserstein means for aligning distributional data"):
  with members:
    - "a gradual semi discrete approach to generative network training via explicit wasserstein minimization"
    - "variational wasserstein clustering"
    - "sliced wasserstein distance for learning gaussian mixture models"
    - "go icp a globally optimal solution to 3d icp point set registration"
    - "deep coral correlation alignment for deep domain adaptation"
    - "facenet a unified embedding for face recognition and clustering"
    - "sliced wasserstein kernels for probability distributions"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-3 (induced by source paper "optimal transport mapping via input convex neural networks"):
  with members:
    - "on scalable and efficient computation of large scale optimal transport"
    - "subspace detours building transport plans that are optimal on subspace projections"
    - "adversarial computation of optimal transport maps"
    - "mode collapse and regularity of optimal transportation maps"
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "generative modeling using the sliced wasserstein distance"
    - "pacgan the power of two samples in generative adversarial networks"
    - "a geometric view of optimal transportation and generative model"

Connected through hyperedge HE-4 (induced by source paper "got an optimal transport framework for graph comparison"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "on the convergence of adam and beyond"
    - "gromov wasserstein alignment of word embedding spaces"
    - "netlsd hearing the shape of a graph"
    - "implicit reparameterization gradients"
    - "learning permutations with sinkhorn policy gradient"
    - "ds a flexible scalable and provably tight relaxation for matching problems"
    - "improved bilinear pooling with cnns"

Connected through hyperedge HE-5 (induced by source paper "subspace detours building transport plans that are optimal on subspace projections"):
  with members:
    - "subspace robust wasserstein distances"
    - "sliced wasserstein generative models"
    - "generative modeling using the sliced wasserstein distance"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "deep hashing network for unsupervised domain adaptation"
    - "optimal transport for domain adaptation"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-6 (induced by source paper "superpixel based color transfer"):
  with members:
    - "superpatchmatch an algorithm for robust correspondences using superpixel patches"
    - "scalp superpixels with contour adherence using linear path"
    - "photo stylistic brush robust style transfer via superpixel based bipartite graph"

Connected through hyperedge HE-7 (induced by source paper "dynamic multi agent assignment via discrete optimal transport"):
  with members:
    - "learning with a wasserstein loss"
    - "decentralized goal assignment and trajectory generation in multi robot networks"
    - "learning probability measures with respect to optimal transport metrics"

Connected through hyperedge HE-8 (induced by source paper "barycenters of natural images constrained wasserstein barycenters for image morphing"):
  with members:
    - "emnist an extension of mnist to handwritten letters"
    - "generative visual manipulation on the natural image manifold"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

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)