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: "stochastic wasserstein barycenters"
  Abstract: We present a stochastic algorithm to compute the barycenter of a set of probability distributions under the Wasserstein metric from optimal transport. Unlike previous approaches, our method extends to continuous input distributions and allows the support of the barycenter to be adjusted in each iteration. We tackle the problem without regularization, allowing us to recover a sharp output whose support is contained within the support of the true barycenter. We give examples where our algorithm recovers a more meaningful barycenter than previous work. Our method is versatile and can be extended to applications such as generating super samples from a given distribution and recovering blue noise approximations.

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"
    - "towards optimal running times for optimal transport"
    - "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 "stochastic optimization for regularized wasserstein estimators"):
  with members:
    - "computational optimal transport complexity by accelerated gradient descent is better than by sinkhorn s algorithm"
    - "unsupervised hyperalignment for multilingual word embeddings"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "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-3 (induced by source paper "unsupervised multilingual alignment using wasserstein barycenter"):
  with members:
    - "gromov wasserstein alignment of word embedding spaces"
    - "unsupervised multilingual word embeddings"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "unsupervised neural machine translation"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (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"
    - "regularized wasserstein means based on variational transportation"
    - "parallel streaming wasserstein barycenters"
    - "multiscale strategies for computing optimal transport"

Connected through hyperedge HE-5 (induced by source paper "learning embeddings into entropic wasserstein spaces"):
  with members:
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "context mover s distance barycenters optimal transport of contexts for building representations"
    - "deep contextualized word representations"
    - "imagenet pre trained models with batch normalization"
    - "node2vec scalable feature learning for networks"
    - "enriching word vectors with subword information"
    - "learning with a wasserstein loss"
    - "order embeddings of images and language"

Connected through hyperedge HE-6 (induced by source paper "gear geometry aware renyi information"):
  with members:
    - "improving gans using optimal transport"
    - "fixing a broken elbo"
    - "improved training of wasserstein gans"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "improved techniques for training gans"
    - "maximizing diversity in biology and beyond"
    - "taming the noise in reinforcement learning via soft updates"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-7 (induced by source paper "alleviating label switching with optimal transport"):
  with members:
    - "gromov wasserstein alignment of word embedding spaces"
    - "the sample complexity of multi reference alignment"
    - "parallel streaming wasserstein barycenters"
    - "multireference alignment using semidefinite programming"

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)