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 "optimal transport mapping via input convex neural networks"):
  Abstract: In this paper, we present a novel and principled approach to learn the optimal transport between two distributions, from samples. Guided by the optimal transport theory, we learn the optimal Kantorovich potential which induces the optimal transport map. This involves learning two convex functions, by solving a novel minimax optimization. Building upon recent advances in the field of input convex neural networks, we propose a new framework where the gradient of one convex function represents the optimal transport mapping. Numerical experiments confirm that we learn the optimal transport mapping. This approach ensures that the transport mapping we find is optimal independent of how we initialize the neural networks. Further, target distributions from a discontinuous support can be easily captured, as gradient of a convex function naturally models a {\em discontinuous} transport mapping.

Includes members:
  Member "on scalable and efficient computation of large scale optimal transport":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "large scale optimal transport via adversarial training with cycle consistency")
  Member "subspace detours building transport plans that are optimal on subspace projections":
    also connected through other hyperedges:
      (none)
  Member "adversarial computation of optimal transport maps":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "large scale optimal transport via adversarial training with cycle consistency")
  Member "mode collapse and regularity of optimal transportation maps":
    also connected through other hyperedges:
      (none)
  Member "improving the improved training of wasserstein gans a consistency term and its dual effect":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sliced wasserstein generative models")
  Member "generative modeling using the sliced wasserstein distance":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning implicit generative models with theoretical guarantees")
  Member "pacgan the power of two samples in generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "a geometric view of optimal transportation and generative model":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "knowledge representing efficient sparse representation of prior knowledge for knowledge distillation")

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.LG(Machine Learning)