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: "on nmt search errors and model errors cat got your tongue"
  Abstract: We report on search errors and model errors in neural machine translation (NMT). We present an exact inference procedure for neural sequence models based on a combination of beam search and depth-first search. We use our exact search to find the global best model scores under a Transformer base model for the entire WMT15 English-German test set. Surprisingly, beam search fails to find these global best model scores in most cases, even with a very large beam size of 100. For more than 50% of the sentences, the model in fact assigns its global best score to the empty translation, revealing a massive failure of neural models in properly accounting for adequacy. We show by constraining search with a minimum translation length that at the root of the problem of empty translations lies an inherent bias towards shorter translations. We conclude that vanilla NMT in its current form requires just the right amount of beam search errors, which, from a modelling perspective, is a highly unsatisfactory conclusion indeed, as the model often prefers an empty translation.

Connected through hyperedge HE-1 (induced by source paper "consistency of a recurrent language model with respect to incomplete decoding"):
  with members:
    - "the curious case of neural text degeneration"
    - "neural text generation with unlikelihood training"
    - "hierarchical neural story generation"
    - "correcting length bias in neural machine translation"
    - "on the state of the art of evaluation in neural language models"
    - "recurrent neural networks as weighted language recognizers"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-2 (induced by source paper "investigating the decoders of maximum likelihood sequence models a look ahead approach"):
  with members:
    - "end to end dense video captioning with masked transformer"
    - "a call for clarity in reporting bleu scores"
    - "tvqa localized compositional video question answering"
    - "optimal completion distillation for sequence learning"
    - "attention is all you need"
    - "adversarial learning for neural dialogue generation"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "multi30k multilingual english german image descriptions"

Connected through hyperedge HE-3 (induced by source paper "the curious case of neural text degeneration"):
  with members:
    - "unifying human and statistical evaluation for natural language generation"
    - "neural text generation with unlikelihood training"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "on accurate evaluation of gans for language generation"
    - "eval all trust a few do wrong to none comparing sentence generation models"
    - "learning to write with cooperative discriminators"
    - "hierarchical neural story generation"
    - "texygen a benchmarking platform for text generation models"

Connected through hyperedge HE-4 (induced by source paper "cost sensitive training for autoregressive models"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "on the weaknesses of reinforcement learning for neural machine translation"
    - "bridging the gap between training and inference for neural machine translation"
    - "non monotonic sequential text generation"
    - "beyond bleu training neural machine translation with semantic similarity"
    - "optimal completion distillation for sequence learning"
    - "loss functions for multiset prediction"
    - "attention is all you need"

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)