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: "stack propagation improved representation learning for syntax"
  Abstract: Traditional syntax models typically leverage part-of-speech (POS) information by constructing features from hand-tuned templates. We demonstrate that a better approach is to utilize POS tags as a regularizer of learned representations. We propose a simple method for learning a stacked pipeline of models which we call "stack-propagation". We apply this to dependency parsing and tagging, where we use the hidden layer of the tagger network as a representation of the input tokens for the parser. At test time, our parser does not require predicted POS tags. On 19 languages from the Universal Dependencies, our method is 1.3% (absolute) more accurate than a state-of-the-art graph-based approach and 2.7% more accurate than the most comparable greedy model.

Connected through hyperedge HE-1 (induced by source paper "is pos tagging necessary or even helpful for neural dependency parsing"):
  with members:
    - "syntax enhanced neural machine translation with syntax aware word representations"
    - "75 languages 1 model parsing universal dependencies universally"
    - "hearst patterns revisited automatic hypernym detection from large text corpora"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "constituency parsing with a self attentive encoder"
    - "deep biaffine attention for neural dependency parsing"

Connected through hyperedge HE-2 (induced by source paper "leveraging pretrained word embeddings for part of speech tagging of code switching data"):
  with members:
    - "named entity recognition on code switched data overview of the calcs 2018 shared task"
    - "part of speech tagging for code switched data"
    - "creating a large multi layered representational repository of linguistic code switched arabic data"
    - "universal dependency parsing for hindi english code switching"
    - "joining hands exploiting monolingual treebanks for parsing of code mixing data"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "word translation without parallel data"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-3 (induced by source paper "neural simile recognition with cyclic multitask learning and local attention"):
  with members:
    - "open domain targeted sentiment analysis via span based extraction and classification"
    - "variational recurrent neural machine translation"
    - "a unified model for opinion target extraction and target sentiment prediction"
    - "modeling localness for self attention networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "self attentional acoustic models"
    - "variational neural discourse relation recognizer"
    - "variational neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "clinical concept extraction for document level coding"):
  with members:
    - "enhancing clinical concept extraction with contextual embeddings"
    - "explainable prediction of medical codes from clinical text"
    - "syntactic scaffolds for semantic structures"
    - "clinical concept extraction with contextual word embedding"
    - "effective feature representation for clinical text concept extraction"
    - "cliner 2 0 accessible and accurate clinical concept extraction"
    - "awe cm vectors augmenting word embeddings with a clinical metathesaurus"
    - "an overview of multi task learning in deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "pseudo task augmentation from deep multitask learning to intratask sharing and back"):
  with members:
    - "evolving deep neural networks"
    - "learned in translation contextualized word vectors"
    - "learning multiple visual domains with residual adapters"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "population based training of neural networks"
    - "distral robust multitask reinforcement learning"
    - "an overview of multi task learning in deep neural networks"
    - "one model to learn them all"

Connected through hyperedge HE-6 (induced by source paper "evolutionary architecture search for deep multitask networks"):
  with members:
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "learning multiple visual domains with residual adapters"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "population based training of neural networks"
    - "distral robust multitask reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "beyond shared hierarchies deep multitask learning through soft layer ordering"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "progressive neural networks"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "deep cross residual learning for multitask visual recognition"
    - "fully adaptive feature sharing in multi task networks with applications in person attribute classification"
    - "deep multi task representation learning a tensor factorisation approach"
    - "cross stitch networks for multi task learning"

Connected through hyperedge HE-8 (induced by source paper "dragnn a transition based framework for dynamically connected neural networks"):
  with members:
    - "globally normalized transition based neural networks"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "distilling an ensemble of greedy dependency parsers into one mst parser"
    - "neural architectures for named entity recognition"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "bidirectional lstm crf models for sequence tagging"
    - "transition based dependency parsing with stack long short term memory"

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)