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 "learning unsupervised word mapping by maximizing mean discrepancy"):
  Abstract: Cross-lingual word embeddings aim to capture common linguistic regularities of different languages, which benefit various downstream tasks ranging from machine translation to transfer learning. Recently, it has been shown that these embeddings can be effectively learned by aligning two disjoint monolingual vector spaces through a linear transformation (word mapping). In this work, we focus on learning such a word mapping without any supervision signal. Most previous work of this task adopts parametric metrics to measure distribution differences, which typically requires a sophisticated alternate optimization process, either in the form of \emph{minmax game} or intermediate \emph{density estimation}. This alternate optimization process is relatively hard and unstable. In order to avoid such sophisticated alternate optimization, we propose to learn unsupervised word mapping by directly maximizing the mean discrepancy between the distribution of transferred embedding and target embedding. Extensive experimental results show that our proposed model outperforms competitive baselines by a large margin.

Includes members:
  Member "an iterative closest point method for unsupervised word translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "zero resource cross lingual named entity recognition")
  Member "unsupervised alignment of embeddings with wasserstein procrustes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "refinement of unsupervised cross lingual word embeddings")
  Member "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "unsupervised cross lingual transfer of word embedding spaces":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sentence transition matrix an efficient approach that preserves sentence semantics")
  Member "unsupervised machine translation using monolingual corpora only":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised question decomposition for question answering")
  Member "unsupervised word mapping using structural similarities in monolingual embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "scalable cross lingual transfer of neural sentence embeddings")
  Member "offline bilingual word vectors orthogonal transformations and the inverted softmax":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "word translation without parallel data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep graph matching consensus")

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)