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 "automatically extracting challenge sets for non local phenomena neural machine translation"):
  Abstract: We show that the state-of-the-art Transformer MT model is not biased towards monotonic reordering (unlike previous recurrent neural network models), but that nevertheless, long-distance dependencies remain a challenge for the model. Since most dependencies are short-distance, common evaluation metrics will be little influenced by how well systems perform on them. We, therefore, propose an automatic approach for extracting challenge sets replete with long-distance dependencies, and argue that evaluation using this methodology provides a complementary perspective on system performance. To support our claim, we compile challenge sets for English-German and German-English, which are much larger than any previously released challenge set for MT. The extracted sets are large enough to allow reliable automatic evaluation, which makes the proposed approach a scalable and practical solution for evaluating MT performance on the long-tail of syntactic phenomena.

Includes members:
  Member "star transformer":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transformer on a diet")
  Member "linguistic evaluation of german english machine translation using a test suite":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on document level machine translation methods and evaluation")
  Member "linguistic knowledge and transferability of contextual representations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on contextual embeddings")
  Member "semantic neural machine translation using amr":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "recurrent graph syntax encoder for neural machine translation")
  Member "semantic structural evaluation for text simplification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "made for each other broad coverage semantic structures meet preposition supersenses")
  Member "do rnns learn human like abstract word order preferences":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "linguistic knowledge and transferability of contextual representations")
  Member "a challenge set for french english machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a comparative analysis of knowledge intensive and data intensive semantic parsers")
  Member "automatic metric validation for grammatical error correction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to combine grammatical error corrections")

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)