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: "multi reference training with pseudo references for neural translation and text generation"
  Abstract: Neural text generation, including neural machine translation, image captioning, and summarization, has been quite successful recently. However, during training time, typically only one reference is considered for each example, even though there are often multiple references available, e.g., 4 references in NIST MT evaluations, and 5 references in image captioning data. We first investigate several different ways of utilizing multiple human references during training. But more importantly, we then propose an algorithm to generate exponentially many pseudo-references by first compressing existing human references into lattices and then traversing them to generate new pseudo-references. These approaches lead to substantial improvements over strong baselines in both machine translation (+1.5 BLEU) and image captioning (+3.1 BLEU / +11.7 CIDEr).

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"
    - "ensemble sequence level training for multimodal mt osu baidu wmt18 multimodal machine translation system report"

Connected through hyperedge HE-2 (induced by source paper "learning to stop in structured prediction for neural machine translation"):
  with members:
    - "ensemble sequence level training for multimodal mt osu baidu wmt18 multimodal machine translation system report"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "revisiting challenges in data to text generation with fact grounding"):
  with members:
    - "learning to select track and generate for data to text"
    - "data to text generation with entity modeling"
    - "step by step separating planning from realization in neural data to text generation"
    - "end to end content and plan selection for data to text generation"
    - "describing a knowledge base"
    - "data to text generation with content selection and planning"
    - "learning to generate one sentence biographies from wikidata"
    - "challenges in data to document generation"

Connected through hyperedge HE-4 (induced by source paper "simultaneous translation with flexible policy via restricted imitation learning"):
  with members:
    - "learning to stop in structured prediction for neural machine translation"
    - "incremental decoding and training methods for simultaneous translation in neural machine translation"
    - "you may not need attention"
    - "stacl simultaneous translation with integrated anticipation and controllable latency"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "ensemble sequence level training for multimodal mt osu baidu wmt18 multimodal machine translation system report"):
  with members:
    - "classify or select neural architectures for extractive document summarization"
    - "improved image captioning via policy gradient optimization of spider"
    - "neural system combination for machine translation"
    - "findings of the second shared task on multimodal machine translation and multilingual image description"
    - "osu multimodal machine translation system report"
    - "multi30k multilingual english german image descriptions"
    - "self critical sequence training for image captioning"
    - "a neural attention model for abstractive sentence summarization"

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)