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: "exploiting linguistic resources for neural machine translation using multi task learning"
  Abstract: Linguistic resources such as part-of-speech (POS) tags have been extensively used in statistical machine translation (SMT) frameworks and have yielded better performances. However, usage of such linguistic annotations in neural machine translation (NMT) systems has been left under-explored. #R##N#In this work, we show that multi-task learning is a successful and a easy approach to introduce an additional knowledge into an end-to-end neural attentional model. By jointly training several natural language processing (NLP) tasks in one system, we are able to leverage common information and improve the performance of the individual task. #R##N#We analyze the impact of three design decisions in multi-task learning: the tasks used in training, the training schedule, and the degree of parameter sharing across the tasks, which is defined by the network architecture. The experiments are conducted for an German to English translation task. As additional linguistic resources, we exploit POS information and named-entities (NE). Experiments show that the translation quality can be improved by up to 1.5 BLEU points under the low-resource condition. The performance of the POS tagger is also improved using the multi-task learning scheme.

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"
    - "scheduled multi task learning from syntax to translation"
    - "learning to multitask"
    - "opennmt open source toolkit for neural machine translation"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"

Connected through hyperedge HE-2 (induced by source paper "mix review alleviate forgetting in the pretrain finetune framework for neural language generation models"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "an empirical evaluation of multi task learning in deep neural networks for natural language processing"
    - "the curious case of neural text degeneration"
    - "pretraining methods for dialog context representation learning"
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "real or fake learning to discriminate machine from human generated text"
    - "mass masked sequence to sequence pre training for language generation"

Connected through hyperedge HE-3 (induced by source paper "recurrent graph syntax encoder for neural machine translation"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "syntax enhanced neural machine translation with syntax aware word representations"
    - "semantic neural machine translation using amr"
    - "weighted transformer network for machine translation"
    - "tied multitask learning for neural speech translation"
    - "self attention with relative position representations"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "graph to sequence learning using gated graph neural networks"

Connected through hyperedge HE-4 (induced by source paper "latent part of speech sequences for neural machine translation"):
  with members:
    - "towards robust neural machine translation"
    - "a tree based decoder for neural machine translation"
    - "discrete structural planning for neural machine translation"
    - "top down tree structured decoding with syntactic connections for neural machine translation and parsing"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "learning to parse and translate improves neural machine translation"
    - "syntax directed attention for neural machine translation"
    - "attention is all you need"

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"
    - "scheduled multi task learning from syntax to translation"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "improving interpretability of word embeddings by generating definition and usage"):
  with members:
    - "incorporating sememes into chinese definition modeling"
    - "xsense learning sense separated sparse representations and textual definitions for explainable word sense networks"
    - "deep contextualized word representations"
    - "semi supervised sequence modeling with cross view training"
    - "conditional generators of words definitions"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "definition modeling learning to define word embeddings in natural language"

Connected through hyperedge HE-7 (induced by source paper "an augmented transformer architecture for natural language generation tasks"):
  with members:
    - "self attention with relative position representations"
    - "allennlp a deep semantic natural language processing platform"
    - "end to end dense video captioning with masked transformer"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "recurrent additive networks"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "improving zero shot translation with language independent constraints"):
  with members:
    - "consistency by agreement in zero shot neural machine translation"
    - "effective cross lingual transfer of neural machine translation models without shared vocabularies"
    - "the missing ingredient in zero shot neural machine translation"
    - "universal neural machine translation for extremely low resource languages"
    - "a neural interlingua for multilingual machine translation"
    - "contextual parameter generation for universal neural machine translation"
    - "a teacher student framework for zero resource neural machine translation"
    - "learning joint multilingual sentence representations with neural machine translation"

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)