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 "role of morphology injection in smt a case study from indian language perspective"):
  Abstract: Phrase-based Statistical Machine Translation (PBSMT) is commonly used for automatic translation. However, PBSMT runs into difficulty when either or both of the source and target languages are morphologically rich. Factored models are found to be useful for such cases, as they consider word as a vector of factors. These factors can contain any information about the surface word and use it while translating. The objective of the current work is to handle morphological inflections in Hindi, Marathi, and Malayalam using Factored translation models when translating from English. Statistical MT approaches face the problem of data sparsity when translating to a morphologically rich language. It is very unlikely for a parallel corpus to contain all morphological forms of words. We propose a solution to generate these unseen morphological forms and inject them into the original training corpus. We propose a simple and effective solution based on enriching the input with various morphological forms of words. We observe that morphology injection improves the quality of translation in terms of both adequacy and fluency. We verify this with experiments on three morphologically rich languages when translating from English. From the detailed evaluations, we observed an order of magnitude improvement in translation quality.

Includes members:
  Member "a case study on english malayalam machine translation":
    also connected through other hyperedges:
      (none)
  Member "lexical resources for hindi marathi mt":
    also connected through other hyperedges:
      (none)
  Member "improving statistical machine translation for a resource poor language using related resource rich languages":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exploring benefits of transfer learning in 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)