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 "semantic web for machine translation challenges and directions"):
  Abstract: A large number of machine translation approaches have recently been developed to facilitate the fluid migration of content across languages. However, the literature suggests that many obstacles must still be dealt with to achieve better automatic translations. One of these obstacles is lexical and syntactic ambiguity. A promising way of overcoming this problem is using Semantic Web technologies. This article is an extended abstract of our systematic review on machine translation approaches that rely on Semantic Web technologies for improving the translation of texts. Overall, we present the challenges and opportunities in the use of Semantic Web technologies in Machine Translation. Moreover, our research suggests that while Semantic Web technologies can enhance the quality of machine translation outputs for various problems, the combination of both is still in its infancy.

Includes members:
  Member "augmenting neural machine translation with knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards information rich logical text generation with knowledge enhanced neural models")
  Member "lidioms a multilingual linked idioms data set":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "machine translation using semantic web technologies a survey")
  Member "machine translation using semantic web technologies a survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "augmenting neural machine translation with knowledge graphs")
  Member "mag a multilingual knowledge base agnostic and deterministic entity linking approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "augmenting neural machine translation with knowledge graphs")
  Member "six challenges for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a brief survey of multilingual neural machine translation")

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)