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: "parallel streaming wasserstein barycenters"
  Abstract: Efficiently aggregating data from different sources is a challenging problem, particularly when samples from each source are distributed differently. These differences can be inherent to the inference task or present for other reasons: sensors in a sensor network may be placed far apart, affecting their individual measurements. Conversely, it is computationally advantageous to split Bayesian inference tasks across subsets of data, but data need not be identically distributed across subsets. One principled way to fuse probability distributions is via the lens of optimal transport: the Wasserstein barycenter is a single distribution that summarizes a collection of input measures while respecting their geometry. However, computing the barycenter scales poorly and requires discretization of all input distributions and the barycenter itself. Improving on this situation, we present a scalable, communication-efficient, parallel algorithm for computing the Wasserstein barycenter of arbitrary distributions. Our algorithm can operate directly on continuous input distributions and is optimized for streaming data. Our method is even robust to nonstationary input distributions and produces a barycenter estimate that tracks the input measures over time. The algorithm is semi-discrete, needing to discretize only the barycenter estimate. To the best of our knowledge, we also provide the first bounds on the quality of the approximate barycenter as the discretization becomes finer. Finally, we demonstrate the practical effectiveness of our method, both in tracking moving distributions on a sphere, as well as in a large-scale Bayesian inference task.

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"
    - "approximating optimal transport with linear programs"

Connected through hyperedge HE-2 (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"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "sublinear optimization for machine learning"

Connected through hyperedge HE-3 (induced by source paper "variational wasserstein barycenters for geometric clustering"):
  with members:
    - "regularized wasserstein means for aligning distributional data"
    - "pytorch an imperative style high performance deep learning library"
    - "variational wasserstein clustering"
    - "stochastic wasserstein barycenters"
    - "regularized wasserstein means based on variational transportation"
    - "multiscale strategies for computing optimal transport"

Connected through hyperedge HE-4 (induced by source paper "learning transport cost from subset correspondence"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "deep transfer learning with joint adaptation networks"
    - "optimal transport for domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "alleviating label switching with optimal transport"):
  with members:
    - "gromov wasserstein alignment of word embedding spaces"
    - "stochastic wasserstein barycenters"
    - "the sample complexity of multi reference alignment"
    - "multireference alignment using semidefinite programming"

Connected through hyperedge HE-6 (induced by source paper "distributed function minimization in apache spark"):
  with members:
    - "bpr bayesian personalized ranking from implicit feedback"
    - "a reliable effective terascale linear learning system"
    - "graphlab a new framework for parallel machine learning"

Connected through hyperedge HE-7 (induced by source paper "stochastic wasserstein barycenters"):
  with members:
    - "sliced wasserstein kernel for persistence diagrams"
    - "optimal transport for domain adaptation"
    - "super samples from kernel herding"

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.LG(Machine Learning)