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: "towards optimal running times for optimal transport"
  Abstract: In this work, we provide faster algorithms for approximating the optimal transport distance, e.g. earth mover's distance, between two discrete probability distributions $\mu, \nu \in \Delta^n$. Given a cost function $C : [n] \times [n] \to \mathbb{R}_{\geq 0}$ where $C(i,j)$ quantifies the penalty of transporting a unit of mass from $i$ to $j$, we show how to compute a coupling $X$ between $r$ and $c$ in time $\widetilde{O}\left(n^2 /\epsilon \right)$ whose expected transportation cost is within an additive $\epsilon$ of optimal, where we have assumed that the largest entry of $C$ is bounded by a constant. This improves upon the previously best known running time for this problem of $\widetilde{O}\left(\min\left\{ n^{9/4}/\epsilon, n^2/\epsilon^2 \right\}\right)$. #R##N#We achieve our results by providing reductions from optimal transport to canonical optimization problems for which recent algorithmic efforts have provided nearly-linear time algorithms. Leveraging nearly linear time algorithms for solving packing linear programs and for solving the matrix balancing problem, we obtain two separate proofs of our stated running time. #R##N#Moreover, we show that further algorithmic improvements to our result would be surprising in the sense that any improvement would yield an $o(n^{2.5})$ algorithm for \textit{maximum cardinality bipartite matching}, for which currently the only known algorithms for achieving such a result are based on fast-matrix multiplication.

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"
    - "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"
    - "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"
    - "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"
    - "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:
    - "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"
    - "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 efficient optimal transport an analysis of greedy and accelerated mirror descent algorithms"):
  with members:
    - "computational optimal transport complexity by accelerated gradient descent is better than by sinkhorn s algorithm"
    - "better and simpler error analysis of the sinkhorn knopp algorithm for matrix scaling"
    - "approximating optimal transport with linear programs"
    - "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"
    - "sliced wasserstein kernel for persistence diagrams"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-5 (induced by source paper "on the acceleration of the sinkhorn and greenkhorn algorithms for optimal transport"):
  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"
    - "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-6 (induced by source paper "a graph theoretic additive approximation of optimal transport"):
  with members:
    - "on efficient optimal transport an analysis of greedy and accelerated mirror descent algorithms"
    - "preconditioning for the geometric transportation problem"
    - "computational optimal transport complexity by accelerated gradient descent is better than by sinkhorn s algorithm"
    - "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-7 (induced by source paper "approximating optimal transport with linear programs"):
  with members:
    - "computational optimal transport complexity by accelerated gradient descent is better than by sinkhorn s algorithm"
    - "better and simpler error analysis of the sinkhorn knopp algorithm for matrix scaling"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"
    - "a nearly linear time ptas for explicit fractional packing and covering linear programs"
    - "nearly linear time approximation schemes for mixed packing covering and facility location linear programs"

Connected through hyperedge HE-8 (induced by source paper "approximating the quadratic transportation metric in near linear time"):
  with members:
    - "computational optimal transport complexity by accelerated gradient descent is better than by sinkhorn s algorithm"
    - "approximate nearest neighbor search in high dimensions"
    - "approximating optimal transport with linear programs"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "optimal transport for domain adaptation"
    - "explicit approximations of the gaussian kernel"

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)