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: "approximating optimal transport with linear programs"
  Abstract: In the regime of bounded transportation costs, additive approximations for the optimal transport problem are reduced (rather simply) to relative approximations for positive linear programs, resulting in faster additive approximation algorithms for optimal transport.

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"
    - "towards optimal running times for optimal transport"
    - "semidual regularized optimal transport"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"

Connected through hyperedge HE-2 (induced by source paper "fliptest fairness testing via optimal transport"):
  with members:
    - "adversarial computation of optimal transport maps"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "fairness under composition"
    - "scalable unbalanced optimal transport using generative adversarial networks"
    - "automated test generation to detect individual discrimination in ai models"
    - "hunting for discriminatory proxies in linear regression models"
    - "an empirical study of rich subgroup fairness for machine learning"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"

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"
    - "towards optimal running times for optimal transport"
    - "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"
    - "towards optimal running times for optimal transport"
    - "better and simpler error analysis of the sinkhorn knopp algorithm for matrix scaling"
    - "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"
    - "towards optimal running times for optimal transport"
    - "better and simpler error analysis of the sinkhorn knopp algorithm for matrix scaling"
    - "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"
    - "towards optimal running times for optimal transport"
    - "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 "towards optimal running times for optimal transport"):
  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"
    - "much faster algorithms for matrix scaling"
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"
    - "sparsified cholesky solvers for sdd linear systems"
    - "efficient inverse maintenance and faster algorithms for linear programming"
    - "nearly linear time packing and covering lp solvers"

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"
    - "towards optimal running times for optimal transport"
    - "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)