human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "convex histogram based joint image segmentation with regularized optimal transport cost"):
  Abstract: We investigate in this work a versatile convex framework for multiple image segmentation, relying on the regularized optimal mass transport theory. In this setting, several transport cost functions are considered and used to match statistical distributions of features. In practice, global multidimensional histograms are estimated from the segmented image regions, and are compared to referring models that are either fixed histograms given a priori, or directly inferred in the non-supervised case. The different convex problems studied are solved efficiently using primal-dual algorithms. The proposed approach is generic and enables multi-phase segmentation as well as co-segmentation of multiple images.

Includes members:
  Member "convex color image segmentation with optimal transport distances":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "volume preserving image segmentation with entropic regularization optimal transport and its applications in deep learning")
  Member "fast optimal transport averaging of neuroimaging data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial computation of optimal transport maps")
  Member "an inertial forward backward algorithm for monotone inclusions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "convex color image segmentation with optimal transport distances")
  Member "learning with a wasserstein loss":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "regularizing activations in neural networks via distribution matching with the wasserstein metric")
  Member "globally optimal joint image segmentation and shape matching based on wasserstein modes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a combinatorial solution to non rigid 3d shape to image matching")
  Member "imaging with kantorovich rubinstein discrepancy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transport based analysis modeling and learning from signal and data distributions")
  Member "regularized discrete optimal transport":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "large scale optimal transport via adversarial training with cycle consistency")
  Member "total variation minimization and graph cuts for moving objects segmentation":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the source paper 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)