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: "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
  Abstract: Computing optimal transport distances such as the earth mover's distance is a fundamental problem in machine learning, statistics, and computer vision. Despite the recent introduction of several algorithms with good empirical performance, it is unknown whether general optimal transport distances can be approximated in near-linear time. This paper demonstrates that this ambitious goal is in fact achieved by Cuturi's Sinkhorn Distances. This result relies on a new analysis of Sinkhorn iteration, which also directly suggests a new greedy coordinate descent algorithm, Greenkhorn, with the same theoretical guarantees. Numerical simulations illustrate that Greenkhorn significantly outperforms the classical Sinkhorn algorithm in practice.

Connected through hyperedge HE-1 (induced by source paper "geometric dataset distances via optimal transport"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "dynamics and reachability of learning tasks"
    - "hierarchical optimal transport for document representation"
    - "transferability and hardness of supervised classification tasks"
    - "task2vec task embedding for meta learning"
    - "learning embeddings into entropic wasserstein spaces"
    - "gromov wasserstein alignment of word embedding spaces"
    - "deep learning for classical japanese literature"

Connected through hyperedge HE-2 (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"
    - "approximating optimal transport with linear programs"

Connected through hyperedge HE-3 (induced by source paper "stochastic optimization for regularized wasserstein estimators"):
  with members:
    - "computational optimal transport complexity by accelerated gradient descent is better than by sinkhorn s algorithm"
    - "stochastic wasserstein barycenters"
    - "unsupervised hyperalignment for multilingual word embeddings"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "parallel streaming wasserstein barycenters"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "sublinear optimization for machine learning"

Connected through hyperedge HE-4 (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"
    - "towards optimal running times for optimal transport"
    - "scalable unbalanced optimal transport using generative adversarial networks"
    - "learning with a wasserstein loss"

Connected through hyperedge HE-5 (induced by source paper "supervised quantile normalization for low rank matrix factorization"):
  with members:
    - "unconstrained monotonic neural networks"
    - "computational optimal transport complexity by accelerated gradient descent is better than by sinkhorn s algorithm"
    - "monotonic calibrated interpolated look up tables"
    - "exact matrix completion via convex optimization"
    - "a direct formulation for sparse pca using semidefinite programming"

Connected through hyperedge HE-6 (induced by source paper "a data dependent algorithm for querying earth mover s distance with low doubling dimension"):
  with members:
    - "conditional hardness of earth mover distance"
    - "optimal transport for domain adaptation"
    - "a survey on truth discovery"
    - "on constant factor approximation for earth mover distance over doubling metrics"
    - "fast construction of nets in low dimensional metrics and their applications"

Connected through hyperedge HE-7 (induced by source paper "convergence rates of smooth message passing with rounding in entropy regularized map inference"):
  with members:
    - "convergent message passing algorithms for inference over general graphs with convex free energies"
    - "map estimation linear programming and belief propagation with convex free energies"
    - "convexifying the bethe free energy"

Connected through hyperedge HE-8 (induced by source paper "stochastic proximal algorithms with son regularization towards efficient optimal transport for domain adaptation"):
  with members:
    - "optimal transport for domain adaptation"

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)