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 "structure invariant testing for machine translation"):
  Abstract: In recent years, machine translation software has increasingly been integrated into our daily lives. People routinely use machine translation for various applications, such as describing symptoms to a foreign doctor and reading political news in a foreign language. However, due to the complexity and intractability of neural machine translation (NMT) models that power modern machine translation systems, these systems are far from being robust. They can return inferior results that lead to misunderstanding, medical misdiagnoses, or threats to personal safety. Despite its apparent importance, validating the robustness of machine translation is very difficult and has, therefore, been much under-explored. #R##N#To tackle this challenge, we introduce structure-invariant testing (SIT), a novel, widely applicable metamorphic testing methodology for validating machine translation software. Our key insight is that the translation results of similar source sentences should typically exhibit a similar sentence structure. SIT is designed to leverage this insight to test any machine translation system with unlabeled sentences; it specifically targets mistranslations that are difficult-to-find using state-of-the-art translation quality metrics such as BLEU. We have realized a practical implementation of SIT by (1) substituting one word in a given sentence with semantically similar, syntactically equivalent words to generate similar sentences, and (2) using syntax parse trees (obtained via constituency/dependency parsing) to represent sentence structure. To evaluate SIT, we have used it to test Google Translate and Bing Microsoft Translator with 200 unlabeled sentences as input, which led to 56 and 61 buggy translations with 60% and 61% top-1 accuracy, respectively. The bugs are diverse, including under-translation, over-translation, incorrect modification, word/phrase mistranslation, and unclear logic.

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 "defensive quantization when efficiency meets robustness":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "triple wins boosting accuracy robustness and efficiency together by enabling input adaptive inference")
  Member "adversarial sample detection for deep neural network through model mutation testing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reliability validation of learning enabled vehicle tracking")
  Member "sirenattack generating adversarial audio for end to end acoustic systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "universal adversarial audio perturbations")
  Member "achieving human parity on automatic chinese to english news translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gret global representation enhanced transformer")
  Member "code2vec learning distributed representations of code":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial robustness for code")
  Member "on adversarial examples for character level neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "linguistic generalization and compositionality in modern artificial neural networks")

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)