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: "cross lingual document retrieval using regularized wasserstein distance"
  Abstract: Many information retrieval algorithms rely on the notion of a good distance that allows to efficiently compare objects of different nature. Recently, a new promising metric called Word Mover's Distance was proposed to measure the divergence between text passages. In this paper, we demonstrate that this metric can be extended to incorporate term-weighting schemes and provide more accurate and computationally efficient matching between documents using entropic regularization. We evaluate the benefits of both extensions in the task of cross-lingual document retrieval (CLDR). Our experimental results on eight CLDR problems suggest that the proposed methods achieve remarkable improvements in terms of Mean Reciprocal Rank compared to several baselines.

Connected through hyperedge HE-1 (induced by source paper "massively multilingual document alignment with cross lingual sentence mover s distance"):
  with members:
    - "a massive collection of cross lingual web document pairs"
    - "hierarchical document encoder for parallel corpus mining"
    - "improving multilingual sentence embedding using bi directional dual encoder with additive margin softmax"
    - "cross lingual language model pretraining"
    - "effective parallel corpus mining using bilingual sentence embeddings"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "an empirical analysis of nmt derived interlingual embeddings and their use in parallel sentence identification"
    - "linear complexity relaxed word mover s distance with gpu acceleration"

Connected through hyperedge HE-2 (induced by source paper "wasserstein distances for evaluating cross lingual embeddings"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "a corpus for multilingual document classification in eight languages"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "conceptnet at semeval 2017 task 2 extending word embeddings with multilingual relational knowledge"
    - "word translation without parallel data"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "adversarial deep averaging networks for cross lingual sentiment classification"

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)