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 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.

Connected through hyperedge HE-1 (induced by source paper "revisiting fixed support wasserstein barycenter computational hardness and efficient algorithms"):
  with members:
    - "a graph theoretic additive approximation of optimal transport"
    - "on the acceleration of the sinkhorn and greenkhorn algorithms for 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 the acceleration of the sinkhorn and greenkhorn algorithms for optimal transport"
    - "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 "a direct tilde o 1 epsilon iteration parallel algorithm for optimal transport"):
  with members:
    - "a graph theoretic additive approximation of optimal transport"
    - "computational optimal transport complexity by accelerated gradient descent is better than by sinkhorn s algorithm"
    - "towards optimal running times for optimal transport"
    - "approximating optimal transport with linear programs"
    - "approximating the quadratic transportation metric in near linear time"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "much faster algorithms for matrix scaling"
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"

Connected through hyperedge HE-4 (induced by source paper "on the acceleration of the sinkhorn and greenkhorn algorithms for optimal transport"):
  with members:
    - "a graph theoretic additive approximation of optimal transport"
    - "computational optimal transport complexity by accelerated gradient descent is better than by sinkhorn s algorithm"
    - "towards optimal running times for optimal transport"
    - "better and simpler error analysis of the sinkhorn knopp algorithm for matrix scaling"
    - "approximating optimal transport with linear programs"
    - "probabilistic multilevel clustering via composite transportation distance"
    - "approximating the quadratic transportation metric in near linear time"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"

Connected through hyperedge HE-5 (induced by source paper "a graph theoretic additive approximation of optimal transport"):
  with members:
    - "preconditioning for the geometric transportation problem"
    - "computational optimal transport complexity by accelerated gradient descent is better than by sinkhorn s algorithm"
    - "towards optimal running times for optimal transport"
    - "approximating optimal transport with linear programs"
    - "a faster algorithm for minimum cost bipartite matching in minor free graphs"
    - "approximating the quadratic transportation metric in near linear time"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "dynamic planar voronoi diagrams for general distance functions and their algorithmic applications"

Connected through hyperedge HE-6 (induced by source paper "fast algorithms for computational optimal transport and wasserstein barycenter"):
  with members:
    - "computational optimal transport complexity by accelerated gradient descent is better than by sinkhorn s algorithm"
    - "sparsemax and relaxed wasserstein for topic sparsity"
    - "probabilistic multilevel clustering via composite transportation distance"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "optimal transport for domain adaptation"

Connected through hyperedge HE-7 (induced by source paper "sinkhorn algorithm as a special case of stochastic mirror descent"):
  with members:
    - "on the acceleration of the sinkhorn and greenkhorn algorithms for optimal transport"
    - "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)