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 "automatic testing and improvement of machine translation"):
  Abstract: This paper presents TransRepair, a fully automatic approach for testing and repairing the consistency of machine translation systems. TransRepair combines mutation with metamorphic testing to detect inconsistency bugs (without access to human oracles). It then adopts probability-reference or cross-reference to post-process the translations, in a grey-box or black-box manner, to repair the inconsistencies. Our evaluation on two state-of-the-art translators, Google Translate and Transformer, indicates that TransRepair has a high precision (99%) on generating input pairs with consistent translations. With these tests, using automatic consistency metrics and manual assessment, we find that Google Translate and Transformer have approximately 36% and 40% inconsistency bugs. Black-box repair fixes 28% and 19% bugs on average for Google Translate and Transformer. Grey-box repair fixes 30% bugs on average for Transformer. Manual inspection indicates that the translations repaired by our approach improve consistency in 87% of cases (degrading it in 2%), and that our repairs have better translation acceptability in 27% of the cases (worse in 8%).

Includes members:
  Member "metamorphic testing a new approach for generating next test cases":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "object based metamorphic testing through image structuring")
  Member "machine learning testing survey landscapes and horizons":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "importance driven deep learning system testing")
  Member "training on synthetic noise improves robustness to natural noise in machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust unsupervised neural machine translation with adversarial training")
  Member "modeling recurrence for transformer":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "accessing higher level representations in sequential transformers with feedback memory")
  Member "robust neural machine translation with doubly adversarial inputs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "freelb enhanced adversarial training for natural language understanding")
  Member "on the impact of various types of noise on neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "findings of the first shared task on machine translation robustness")
  Member "towards robust neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust unsupervised neural machine translation with adversarial training")
  Member "achieving human parity on automatic chinese to english news translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gret global representation enhanced transformer")

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.SE(Software Engineering)