human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "revisiting fixed support wasserstein barycenter computational hardness and efficient algorithms"):
  with members:
    - "on efficient optimal transport an analysis of greedy and accelerated mirror descent algorithms"
    - "a graph theoretic additive approximation of optimal transport"
    - "computational optimal transport complexity by accelerated gradient descent is better than by sinkhorn s algorithm"
    - "stochastic wasserstein barycenters"
    - "towards optimal running times for optimal transport"
    - "semidual regularized optimal transport"
    - "approximating optimal transport with linear programs"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"

Connected through hyperedge HE-2 (induced by source paper "on unbalanced optimal transport an analysis of sinkhorn algorithm"):
  with members:
    - "on efficient optimal transport an analysis of greedy and accelerated mirror descent algorithms"
    - "computational optimal transport complexity by accelerated gradient descent is better than by sinkhorn s algorithm"
    - "towards optimal running times for optimal transport"
    - "scalable unbalanced optimal transport using generative adversarial networks"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "learning with a wasserstein loss"

Connected through hyperedge HE-3 (induced by source paper "sinkhorn algorithm as a special case of stochastic mirror descent"):
  with members:
    - "on efficient optimal transport an analysis of greedy and accelerated mirror descent algorithms"
    - "computational optimal transport complexity by accelerated gradient descent is better than by sinkhorn s algorithm"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"

Question: Please tell me which class the center node 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)