human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "massively multilingual document alignment with cross lingual sentence mover s distance"):
  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. Such aligned data can be used for a variety of NLP tasks from training cross-lingual representations to mining parallel bitexts for machine translation training. In this paper we develop an unsupervised scoring function that leverages cross-lingual sentence embeddings to compute the semantic distance between documents in different languages. These semantic distances are then used to guide a document alignment algorithm to properly pair cross-lingual web documents across a variety of low, mid, and high-resource language pairs. Recognizing that our proposed scoring function and other state of the art methods are computationally intractable for long web documents, we utilize a more tractable greedy algorithm that performs comparably. We experimentally demonstrate that our distance metric performs better alignment than current baselines outperforming them by 7% on high-resource language pairs, 15% on mid-resource language pairs, and 22% on low-resource language pairs

Includes members:
  Member "a massive collection of cross lingual web document pairs":
    also connected through other hyperedges:
      (none)
  Member "hierarchical document encoder for parallel corpus mining":
    also connected through other hyperedges:
      (none)
  Member "improving multilingual sentence embedding using bi directional dual encoder with additive margin softmax":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "absent cross lingual sentence representation mapping with bidirectional gans")
  Member "cross lingual language model pretraining":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on contextual embeddings")
  Member "effective parallel corpus mining using bilingual sentence embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "contextual lensing of universal sentence representations")
  Member "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "contextual lensing of universal sentence representations")
  Member "cross lingual document retrieval using regularized wasserstein distance":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "wasserstein distances for evaluating cross lingual embeddings")
  Member "an empirical analysis of nmt derived interlingual embeddings and their use in parallel sentence identification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a bilingual generative transformer for semantic sentence embedding")

Question: Please tell me which class the source paper 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)