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: "convex color image segmentation with optimal transport distances"
  Abstract: This work is about the use of regularized optimal-transport distances for convex, histogram-based image segmentation. In the considered framework, fixed exemplar histograms define a prior on the statistical features of the two regions in competition. In this paper, we investigate the use of various transport-based cost functions as discrepancy measures and rely on a primal-dual algorithm to solve the obtained convex optimization problem.

Connected through hyperedge HE-1 (induced by source paper "volume preserving image segmentation with entropic regularization optimal transport and its applications in deep learning"):
  with members:
    - "a regularized convolutional neural network for semantic image segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "an efficient iterative thresholding method for image segmentation"
    - "convex histogram based joint image segmentation with regularized optimal transport cost"
    - "u net convolutional networks for biomedical image segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-2 (induced by source paper "convex histogram based joint image segmentation with regularized optimal transport cost"):
  with members:
    - "fast optimal transport averaging of neuroimaging data"
    - "an inertial forward backward algorithm for monotone inclusions"
    - "learning with a wasserstein loss"
    - "globally optimal joint image segmentation and shape matching based on wasserstein modes"
    - "imaging with kantorovich rubinstein discrepancy"
    - "regularized discrete optimal transport"
    - "total variation minimization and graph cuts for moving objects segmentation"

Connected through hyperedge HE-3 (induced by source paper "transport based analysis modeling and learning from signal and data distributions"):
  with members:
    - "the radon cumulative distribution transform and its application to image classification"
    - "optimal transport for domain adaptation"
    - "deep learning in neural networks"
    - "sliced wasserstein kernels for probability distributions"
    - "learning with a wasserstein loss"
    - "imaging with kantorovich rubinstein discrepancy"

Connected through hyperedge HE-4 (induced by source paper "relaxed earth mover s distances for chain and tree connected spaces and their use as a loss function in deep learning"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning with a wasserstein loss"
    - "adam a method for stochastic optimization"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"

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)