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 "a graph theoretic additive approximation of optimal transport"):
  Abstract: Transportation cost is an attractive similarity measure between probability distributions due to its many useful theoretical properties. However, solving optimal transport exactly can be prohibitively expensive. Therefore, there has been significant effort towards the design of scalable approximation algorithms. Previous combinatorial results [Sharathkumar, Agarwal STOC '12, Agarwal, Sharathkumar STOC '14] have focused primarily on the design of strongly polynomial multiplicative approximation algorithms. There has also been an effort to design approximate solutions with additive errors [Cuturi NIPS '13, Altschuler et. al NIPS '17, Dvurechensky et al., ICML '18, Quanrud, SOSA '19] within a time bound that is linear in the size of the cost matrix and polynomial in $C/\delta$; here $C$ is the largest value in the cost matrix and $\delta$ is the additive error. We present an adaptation of the classical graph algorithm of Gabow and Tarjan and provide a novel analysis of this algorithm that bounds its execution time by $O(\frac{n^2 C}{\delta}+ \frac{nC^2}{\delta^2})$. Our algorithm is extremely simple and executes, for an arbitrarily small constant $\varepsilon$, only $\lfloor \frac{2C}{(1-\varepsilon)\delta}\rfloor + 1$ iterations, where each iteration consists only of a Dijkstra search followed by a depth-first search. We also provide empirical results that suggest our algorithm significantly outperforms existing approaches in execution time.

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 "preconditioning for the geometric transportation problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "parallel approximate undirected shortest paths via low hop emulators")
  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 "approximating optimal transport with linear programs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "revisiting fixed support wasserstein barycenter computational hardness and efficient algorithms")
  Member "a faster algorithm for minimum cost bipartite matching in minor free graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "min cost flow in unit capacity planar graphs")
  Member "approximating the quadratic transportation metric in near linear time":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a direct tilde o 1 epsilon iteration parallel algorithm for optimal transport")
  Member "near linear time approximation algorithms for optimal transport via sinkhorn iteration":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "geometric dataset distances 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.LG(Machine Learning)