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: "unsupervised alignment of embeddings with wasserstein procrustes"
  Abstract: We consider the task of aligning two sets of points in high dimension, which has many applications in natural language processing and computer vision. As an example, it was recently shown that it is possible to infer a bilingual lexicon, without supervised data, by aligning word embeddings trained on monolingual data. These recent advances are based on adversarial training to learn the mapping between the two embeddings. In this paper, we propose to use an alternative formulation, based on the joint estimation of an orthogonal matrix and a permutation matrix. While this problem is not convex, we propose to initialize our optimization algorithm by using a convex relaxation, traditionally considered for the graph isomorphism problem. We propose a stochastic algorithm to minimize our cost function on large scale problems. Finally, we evaluate our method on the problem of unsupervised word translation, by aligning word embeddings trained on monolingual data. On this task, our method obtains state of the art results, while requiring less computational resources than competing approaches.

Connected through hyperedge HE-1 (induced by source paper "refinement of unsupervised cross lingual word embeddings"):
  with members:
    - "a resource free evaluation metric for cross lingual word embeddings based on graph modularity"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "gromov wasserstein alignment of word embedding spaces"
    - "a discriminative latent variable model for bilingual lexicon induction"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "enriching word vectors with subword information"
    - "massively multilingual word embeddings"
    - "ridge regression hubness and zero shot learning"

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"
    - "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"
    - "stochastic wasserstein barycenters"
    - "unsupervised multilingual word embeddings"
    - "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 "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"):
  with members:
    - "context aware cross lingual mapping"
    - "linguistic knowledge and transferability of contextual representations"
    - "gromov wasserstein alignment of word embedding spaces"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "parameter sharing between dependency parsers for related languages"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"

Connected through hyperedge HE-5 (induced by source paper "robust cross lingual embeddings from parallel sentences"):
  with members:
    - "crosslingual document embedding as reduced rank ridge regression"
    - "a survey of cross lingual word embedding models"
    - "analyzing the limitations of cross lingual word embedding mappings"
    - "better word embeddings by disentangling contextual n gram information"
    - "a corpus for multilingual document classification in eight languages"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "unsupervised multilingual word embeddings"

Connected through hyperedge HE-6 (induced by source paper "density matching for bilingual word embedding"):
  with members:
    - "unsupervised statistical machine translation"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "universal neural machine translation for extremely low resource languages"
    - "near or far wide range zero shot cross lingual dependency parsing"
    - "learning multilingual word embeddings in latent metric space a geometric approach"
    - "non adversarial unsupervised word translation"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-7 (induced by source paper "when deep learning met code search"):
  with members:
    - "on learning meaningful code changes via neural machine translation"
    - "aroma code recommendation via structural code search"
    - "code2vec learning distributed representations of code"
    - "interpretable lstms for whole brain neuroimaging analyses"
    - "analyzing neuroimaging data through recurrent deep learning models"
    - "robust text to sql generation with execution guided decoding"
    - "a deep tree based model for software defect prediction"
    - "semantically enhanced software traceability using deep learning techniques"

Connected through hyperedge HE-8 (induced by source paper "gromov wasserstein learning for graph matching and node embedding"):
  with members:
    - "learning generative models across incomparable spaces"
    - "gromov wasserstein alignment of word embedding spaces"
    - "graph convolutional neural networks for web scale recommender systems"
    - "the gromov wasserstein distance between networks and stable network invariants"
    - "adversarial text generation via feature mover s distance"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "anonymous walk embeddings"
    - "triangular alignment tame a tensor based approach for higher order network alignment"

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)