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 hyperalignment for multilingual word embeddings"
  Abstract: We consider the problem of aligning continuous word representations, learned in multiple languages, to a common space. It was recently shown that, in the case of two languages, it is possible to learn such a mapping without supervision. This paper extends this line of work to the problem of aligning multiple languages to a common space. A solution is to independently map all languages to a pivot language. Unfortunately, this degrades the quality of indirect word translation. We thus propose a novel formulation that ensures composable mappings, leading to better alignments. We evaluate our method by jointly aligning word vectors in eleven languages, showing consistent improvement with indirect mappings while maintaining competitive performance on direct word translation.

Connected through hyperedge HE-1 (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 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-2 (induced by source paper "embcomp visual interactive comparison of vector embeddings"):
  with members:
    - "interpretable latent spaces for learning from demonstration"
    - "visual designs for binned aggregation of multi class scatterplots"
    - "deep contextualized word representations"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "embeddingvis a visual analytics approach to comparative network embedding inspection"
    - "advances in pre training distributed word representations"
    - "node2vec scalable feature learning for networks"
    - "diachronic word embeddings reveal statistical laws of semantic change"

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.CL(Computation and Language)