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 efficient optimal transport an analysis of greedy and accelerated mirror descent algorithms"):
  Abstract: We provide theoretical analyses for two algorithms that solve the regularized optimal transport (OT) problem between two discrete probability measures with at most $n$ atoms. We show that a greedy variant of the classical Sinkhorn algorithm, known as the Greenkhorn algorithm, can be improved to $\widetilde{\mathcal{O}}\left(\frac{n^2}{\varepsilon^2}\right)$, improving on the best known complexity bound of $\widetilde{\mathcal{O}}\left(\frac{n^2}{\varepsilon^3}\right)$. Notably, this matches the best known complexity bound for the Sinkhorn algorithm and helps explain why the Greenkhorn algorithm can outperform the Sinkhorn algorithm in practice. Our proof technique, which is based on a primal-dual formulation and a novel upper bound for the dual solution, also leads to a new class of algorithms that we refer to as adaptive primal-dual accelerated mirror descent (APDAMD) algorithms. We prove that the complexity of these algorithms is $\widetilde{\mathcal{O}}\left(\frac{n^2\gamma^{1/2}}{\varepsilon}\right)$, where $\gamma>0$ refers to the inverse of the strong convexity module of Bregman divergence with respect to $\left\|\cdot\right\|_\infty$. This implies that the APDAMD algorithm is faster than the Sinkhorn and Greenkhorn algorithms in terms of $\varepsilon$. Experimental results on synthetic and real datasets demonstrate the favorable performance of the Greenkhorn and APDAMD algorithms in practice.

Includes members:
  Member "computational optimal transport complexity by accelerated gradient descent is better than by sinkhorn s algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (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-3 (induced by source paper "on the acceleration of the sinkhorn and greenkhorn algorithms for optimal transport")
  Member "approximating optimal transport with linear programs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "revisiting fixed support wasserstein barycenter computational hardness and efficient algorithms")
  Member "sparsemax and relaxed wasserstein for topic sparsity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural oblivious decision ensembles for deep learning on tabular data")
  Member "probabilistic multilevel clustering via composite transportation distance":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the acceleration of the sinkhorn and greenkhorn algorithms for optimal transport")
  Member "near linear time approximation algorithms for optimal transport via sinkhorn iteration":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "geometric dataset distances via optimal transport")
  Member "sliced wasserstein kernel for persistence diagrams":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "understanding the power of persistence pairing via permutation test")

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)