human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "ensemble sequence level training for multimodal mt osu baidu wmt18 multimodal machine translation system report"
  Abstract: This paper describes multimodal machine translation systems developed jointly by Oregon State University and Baidu Research for WMT 2018 Shared Task on multimodal translation. In this paper, we introduce a simple approach to incorporate image information by feeding image features to the decoder side. We also explore different sequence level training methods including scheduled sampling and reinforcement learning which lead to substantial improvements. Our systems ensemble several models using different architectures and training methods and achieve the best performance for three subtasks: En-De and En-Cs in task 1 and (En+De+Fr)-Cs task 1B.

Connected through hyperedge HE-1 (induced by source paper "robust machine translation with domain sensitive pseudo sources baidu osu wmt19 mt robustness shared task system report"):
  with members:
    - "simultaneous translation with flexible policy via restricted imitation learning"
    - "improving robustness of machine translation with synthetic noise"
    - "findings of the first shared task on machine translation robustness"
    - "end to end speech translation with knowledge distillation"
    - "a survey of domain adaptation for neural machine translation"
    - "switchout an efficient data augmentation algorithm for neural machine translation"
    - "robust neural machine translation with joint textual and phonetic embedding"
    - "multi reference training with pseudo references for neural translation and text generation"

Connected through hyperedge HE-2 (induced by source paper "learning to stop in structured prediction for neural machine translation"):
  with members:
    - "multi reference training with pseudo references for neural translation and text generation"
    - "stacl simultaneous translation with integrated anticipation and controllable latency"
    - "breaking the beam search curse a study of re scoring methods and stopping criteria for neural machine translation"
    - "correcting length bias in neural machine translation"
    - "when to finish optimal beam search for neural text generation modulo beam size"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "group sparse cnns for question classification with answer sets"

Question: Please tell me which class the center node 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)