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 "on the acceleration of the sinkhorn and greenkhorn algorithms for optimal transport"):
  Abstract: We propose and analyze a novel approach to accelerate the Sinkhorn and Greenkhorn algorithms for solving the entropic regularized optimal transport (OT) problems. Focusing on the discrete setting where the probability distributions have at most $n$ atoms, and letting $\varepsilon \in \left(0, 1\right)$ denote the tolerance, we introduce accelerated algorithms that have complexity bounds of $\widetilde{\mathcal{O}}\left(n^{7/3}\varepsilon^{-1}\right)$. This improves on the best known complexity bound of $\widetilde{\mathcal{O}} \left(n^2\varepsilon^{-2}\right)$ for the Sinkhorn and Greenkhorn algorithms in terms of $\varepsilon$ and that of $\widetilde{\mathcal{O}}\left(n^{5/2}\varepsilon^{-1}\right)$ for the practical accelerated first-order primal-dual algorithms in terms of $n$. We provide an extensive experimental comparison on both synthetic and real datasets to explore the relative advantages of the new algorithms.

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 "revisiting fixed support wasserstein barycenter computational hardness and efficient algorithms")
  Member "a graph theoretic additive approximation of optimal transport":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "revisiting fixed support wasserstein barycenter computational hardness and efficient algorithms")
  Member "computational optimal transport complexity by accelerated gradient descent is better than by sinkhorn s algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "revisiting fixed support wasserstein barycenter computational hardness and efficient algorithms")
  Member "towards optimal running times for optimal transport":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "revisiting fixed support wasserstein barycenter computational hardness and efficient algorithms")
  Member "better and simpler error analysis of the sinkhorn knopp algorithm for matrix scaling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on efficient optimal transport an analysis of greedy and accelerated mirror descent algorithms")
  Member "approximating optimal transport with linear programs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "revisiting fixed support wasserstein barycenter computational hardness and efficient algorithms")
  Member "probabilistic multilevel clustering via composite transportation distance":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on efficient optimal transport an analysis of greedy and accelerated mirror descent algorithms")
  Member "approximating the quadratic transportation metric in near linear time":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a direct tilde o 1 epsilon iteration parallel algorithm for 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.DS(Data Structures and Algorithms)