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: "a massive collection of cross lingual web document pairs"
  Abstract: Cross-lingual document alignment aims to identify pairs of documents in two distinct languages that are of comparable content or translations of each other. Small-scale efforts have been made to collect aligned document level data on a limited set of language-pairs such as English-German or on limited comparable collections such as Wikipedia. In this paper, we mine twelve snapshots of the Common Crawl corpus and identify web document pairs that are translations of each other. We release a new web dataset consisting of 54 million URL pairs from Common Crawl covering documents in 92 languages paired with English. We evaluate the quality of the dataset by measuring the quality of machine translations from models that have been trained on mined parallel sentence pairs from this aligned corpora and introduce a simple yet effective baseline for identifying these aligned documents. The objective of this dataset and paper is to foster new research in cross-lingual NLP across a variety of low, mid, and high-resource languages.

Connected through hyperedge HE-1 (induced by source paper "massively multilingual document alignment with cross lingual sentence mover s distance"):
  with members:
    - "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"
    - "cross lingual document retrieval using regularized wasserstein distance"
    - "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"

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)