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: "scheduled multi task learning from syntax to translation"
  Abstract: Neural encoder-decoder models of machine translation have achieved impressive results, while learning linguistic knowledge of both the source and target languages in an implicit end-to-end manner. We propose a framework in which our model begins learning syntax and translation interleaved, gradually putting more focus on translation. Using this approach, we achieve considerable improvements in terms of BLEU score on relatively large parallel corpus (WMT14 English to German) and a low-resource (WIT German to English) setup.

Connected through hyperedge HE-1 (induced by source paper "learning to multi task learn for better neural machine translation"):
  with members:
    - "autosem automatic task selection and mixing in multi task learning"
    - "neural machine translation for bilingually scarce scenarios a deep multi task learning approach"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "learning to reweight examples for robust deep learning"
    - "learning to multitask"
    - "opennmt open source toolkit for neural machine translation"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "deeply aggrevated differentiable imitation learning for sequential prediction"

Connected through hyperedge HE-2 (induced by source paper "multi task learning based neural bridging reference resolution"):
  with members:
    - "bam born again multi task networks for natural language understanding"
    - "enhanced word representations for bridging anaphora resolution"
    - "a deterministic algorithm for bridging anaphora resolution"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end neural coreference resolution"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"

Connected through hyperedge HE-3 (induced by source paper "deep contextualized self training for low resource dependency parsing"):
  with members:
    - "viable dependency parsing as sequence labeling"
    - "a survey on recent advances in named entity recognition from deep learning models"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "no training required exploring random encoders for sentence classification"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "distant supervision from disparate sources for low resource part of speech tagging"
    - "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"

Connected through hyperedge HE-4 (induced by source paper "countering the effects of lead bias in news summarization via multi stage training and auxiliary losses"):
  with members:
    - "fine tune bert for extractive summarization"
    - "information asymmetry in kl regularized rl"
    - "content selection in deep learning models of summarization"
    - "learning to extract coherent summary via deep reinforcement learning"
    - "banditsum extractive summarization as a contextual bandit"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"

Connected through hyperedge HE-5 (induced by source paper "promoting the knowledge of source syntax in transformer nmt is not needed"):
  with members:
    - "neural machine translation for bilingually scarce scenarios a deep multi task learning approach"
    - "training tips for the transformer model"
    - "the importance of being recurrent for modeling hierarchical structure"
    - "linguistically informed self attention for semantic role labeling"
    - "learning to parse and translate improves neural machine translation"
    - "predicting target language ccg supertags improves neural machine translation"
    - "attention is all you need"
    - "modeling target side inflection in neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "diversify your datasets analyzing generalization via controlled variance in adversarial datasets"):
  with members:
    - "inoculation by fine tuning a method for analyzing challenge datasets"
    - "still a pain in the neck evaluating text representations on lexical composition"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "multi task deep neural networks for natural language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "collecting diverse natural language inference problems for sentence representation evaluation"

Connected through hyperedge HE-7 (induced by source paper "few shot and zero shot learning for historical text normalization"):
  with members:
    - "a large scale comparison of historical text normalization systems"
    - "tied multitask learning for neural speech translation"
    - "xnmt the extensible neural machine translation toolkit"
    - "an evaluation of neural machine translation models on historical spelling normalization"
    - "evaluating historical text normalization systems how well do they generalize"
    - "unimorph 2 0 universal morphology"
    - "one shot neural cross lingual transfer for paradigm completion"
    - "identifying beneficial task relations for multi task learning in deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "adaptive scheduling for multi task learning"):
  with members:
    - "autoloss learning discrete schedules for alternate optimization"
    - "deep contextualized word representations"
    - "end to end multi task learning with attention"
    - "multi task learning as multi objective optimization"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "automated curriculum learning for neural networks"
    - "teacher student curriculum learning"
    - "online learning rate adaptation with hypergradient descent"

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)