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 "revisiting fixed support wasserstein barycenter computational hardness and efficient algorithms"):
  Abstract: We study the fixed-support Wasserstein barycenter problem (FS-WBP), which consists in computing the Wasserstein barycenter of $m$ discrete probability measures supported on a finite metric space of size $n$. We show first that the constraint matrix arising from the linear programming (LP) representation of the FS-WBP is totally unimodular when $m \geq 3$ and $n = 2$, but not totally unimodular when $m \geq 3$ and $n \geq 3$. This result answers an open problem, since it shows that the FS-WBP is not a minimum-cost flow problem and therefore cannot be solved efficiently using linear programming. Building on this negative result, we propose and analyze a simple and efficient variant of the iterative Bregman projection (IBP) algorithm, currently the most widely adopted algorithm to solve the FS-WBP. The algorithm is an accelerated IBP algorithm which achieves the complexity bound of $\widetilde{\mathcal{O}}(mn^{7/3}/\varepsilon)$. This bound is better than that obtained for the standard IBP algorithm---$\widetilde{\mathcal{O}}(mn^{2}/\varepsilon^2)$---in terms of $\varepsilon$, and that of accelerated primal-dual gradient algorithm---$\widetilde{\mathcal{O}}(mn^{5/2}/\varepsilon)$---in terms of $n$. Empirical studies on simulated datasets demonstrate that the acceleration promised by the theory is real in practice.

Includes members:
  Member "on efficient optimal transport an analysis of greedy and accelerated mirror descent algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on unbalanced optimal transport an analysis of sinkhorn algorithm")
  Member "a graph theoretic additive approximation of optimal transport":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a direct tilde o 1 epsilon iteration parallel algorithm for optimal transport")
  Member "on the acceleration of the sinkhorn and greenkhorn algorithms for optimal transport":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on unbalanced optimal transport an analysis of sinkhorn algorithm")
  Member "computational optimal transport complexity by accelerated gradient descent is better than by sinkhorn s algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stochastic optimization for regularized wasserstein estimators")
  Member "stochastic wasserstein barycenters":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stochastic optimization for regularized wasserstein estimators")
  Member "towards optimal running times for optimal transport":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on unbalanced optimal transport an analysis of sinkhorn algorithm")
  Member "semidual regularized optimal transport":
    also connected through other hyperedges:
      (none)
  Member "approximating optimal transport with linear programs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fliptest fairness testing via optimal transport")

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.CC(Computational Complexity)