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: "imaging with kantorovich rubinstein discrepancy"
  Abstract: We propose the use of the Kantorovich-Rubinstein norm from optimal transport in imaging problems. In particular, we discuss a variational regularisation model endowed with a Kantorovich-Rubinstein discrepancy term and total variation regularization in the context of image denoising and cartoon-texture decomposition. We point out connections of this approach to several other recently proposed methods such as total generalized variation and norms capturing oscillating patterns. We also show that the respective optimization problem can be turned into a convex-concave saddle point problem with simple constraints and hence, can be solved by standard tools. Numerical examples exhibit interesting features and favourable performance for denoising and cartoon-texture decomposition.

Connected through hyperedge HE-1 (induced by source paper "convex histogram based joint image segmentation with regularized optimal transport cost"):
  with members:
    - "convex color image segmentation with optimal transport distances"
    - "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"
    - "regularized discrete optimal transport"
    - "total variation minimization and graph cuts for moving objects segmentation"

Connected through hyperedge HE-2 (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"
    - "convex color image segmentation with optimal transport distances"
    - "optimal transport for domain adaptation"
    - "deep learning in neural networks"
    - "sliced wasserstein kernels for probability distributions"
    - "learning with a wasserstein loss"

Connected through hyperedge HE-3 (induced by source paper "directional global three part image decomposition"):
  with members:
    - "filter design and performance evaluation for fingerprint image segmentation"
    - "global variational method for fingerprint segmentation by three part decomposition"
    - "the shortlist method for fast computation of the earth mover s distance and finding optimal solutions to transportation problems"
    - "separating the real from the synthetic minutiae histograms as fingerprints of fingerprints"
    - "curved region based ridge frequency estimation and curved gabor filters for fingerprint image enhancement"

Connected through hyperedge HE-4 (induced by source paper "a transportation l p distance for signal analysis"):
  with members:
    - "image labeling by assignment"
    - "transport based analysis modeling and learning from signal and data distributions"
    - "the radon cumulative distribution transform and its application to image classification"
    - "the cumulative distribution transform and linear pattern classification"
    - "learning with a wasserstein loss"

Connected through hyperedge HE-5 (induced by source paper "convex color image segmentation with optimal transport distances"):
  with members:
    - "an inertial forward backward algorithm for monotone inclusions"
    - "regularized discrete optimal transport"

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)