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 "beyond bleu training neural machine translation with semantic similarity"):
  Abstract: While most neural machine translation (NMT) systems are still trained using maximum likelihood estimation, recent work has demonstrated that optimizing systems to directly improve evaluation metrics such as BLEU can substantially improve final translation accuracy. However, training with BLEU has some limitations: it doesn't assign partial credit, it has a limited range of output values, and it can penalize semantically correct hypotheses if they differ lexically from the reference. In this paper, we introduce an alternative reward function for optimizing NMT systems that is based on recent work in semantic similarity. We evaluate on four disparate languages translated to English, and find that training with our proposed metric results in better translations as evaluated by BLEU, semantic similarity, and human evaluation, and also that the optimization procedure converges faster. Analysis suggests that this is because the proposed metric is more conducive to optimization, assigning partial credit and providing more diversity in scores than BLEU.

Includes members:
  Member "compare mt a tool for holistic comparison of language generation systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "findings of the first shared task on machine translation robustness")
  Member "simple and effective paraphrastic similarity from parallel translations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multilingual alignment of contextual word representations")
  Member "unsupervised learning of sentence embeddings using compositional n gram features":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sentence analogies exploring linguistic relationships and regularities in sentence embeddings")
  Member "supervised learning of universal sentence representations from natural language inference data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on contextual embeddings")
  Member "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "modelling latent skills for multitask language generation")
  Member "classical structured prediction losses for sequence to sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on feature normalization and data augmentation")
  Member "convolutional sequence to sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "revisiting recurrent networks for paraphrastic sentence embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "structural aware sentence similarity with recursive optimal transport")

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)