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 "cross lingual syntactic transfer with limited resources"):
  Abstract: We describe a simple but effective method for cross-lingual syntactic transfer of dependency parsers, in the scenario where a large amount of translation data is not available.The method makes use of three steps: 1) a method for deriving cross-lingual word clusters, that can then be used in a multilingual parser; 2) a method for transferring lexical information from a target language to source language treebanks; 3) a method for integrating these steps with the density-driven annotation projection method of Rasooli and Collins(2015). Experiments show improvements over the state-of-the-art in several languages used in previous work (Rasooli and Collins, 2015;Zhang and Barzilay, 2015; Ammar et al.,2016), in a setting where the only source of translation data is the Bible, a considerably smaller corpus than the Europarl corpus used in previous work. Results using the Europarl corpus as a source of translation data show additional improvements over the results of Rasooli and Collins (2015). We conclude with results on 38 datasets (26 languages) from the Universal Dependencies corpora: 13 datasets(10 languages) have unlabeled attachment ac-curacies of 80% or higher; the average unlabeled accuracy on the 38 datasets is 74.8%.

Includes members:
  Member "cross lingual dependency parsing with late decoding for truly low resource languages":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cross lingual dependency parsing with unlabeled auxiliary languages")
  Member "the galactic dependencies treebanks getting more data by synthesizing new languages":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improving cross lingual model transfer by chunking")
  Member "parsing universal dependencies without training":
    also connected through other hyperedges:
      (none)
  Member "many languages one parser":
    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 "yara parser a fast and accurate dependency parser":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a generative model for punctuation in dependency trees")

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)