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: "polyglot semantic role labeling"
  Abstract: Previous approaches to multilingual semantic dependency parsing treat languages independently, without exploiting the similarities between semantic structures across languages. We experiment with a new approach where we combine resources from a pair of languages in the CoNLL 2009 shared task to build a polyglot semantic role labeler. Notwithstanding the absence of parallel data, and the dissimilarity in annotations between languages, our approach results in an improvement in SRL performance on multiple languages over a monolingual baseline. Analysis of the polyglot model shows it to be advantageous in lower-resource settings.

Connected through hyperedge HE-1 (induced by source paper "semantic role labeling with iterative structure refinement"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "second order semantic dependency parsing with end to end neural networks"
    - "capturing argument interaction in semantic role labeling with capsule networks"
    - "benchmarking approximate inference methods for neural structured prediction"
    - "achieving human parity on automatic chinese to english news translation"
    - "image segmentation by iterative inference from conditional score estimation"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "exploiting semantics in neural machine translation with graph convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "a comparison of architectures and pretraining methods for contextualized multilingual word embeddings"):
  with members:
    - "cross lingual language model pretraining"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "xnli evaluating cross lingual sentence representations"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "deep contextualized word representations"
    - "multi source cross lingual model transfer learning what to share"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "translate and label an encoder decoder approach for cross lingual semantic role labeling"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "a span selection model for semantic role labeling"
    - "allennlp a deep semantic natural language processing platform"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "learning word vectors for 157 languages"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "polyglot contextual representations improve crosslingual transfer"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "universal dependency parsing from scratch"
    - "cross lingual language model pretraining"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "parameter sharing between dependency parsers for related languages"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "a survey of cross lingual features for zero shot cross lingual semantic parsing"):
  with members:
    - "cross lingual semantic parsing"
    - "exploring neural methods for parsing discourse representation structures"
    - "evaluating scoped meaning representations"
    - "coarse to fine decoding for neural semantic parsing"
    - "neural networks for cross lingual negation scope detection"
    - "universal semantic parsing"
    - "cross lingual abstract meaning representation parsing"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "capturing argument interaction in semantic role labeling with capsule networks"):
  with members:
    - "information aggregation for multi head attention with routing by agreement"
    - "dependency or span end to end uniform semantic role labeling"
    - "multi labeled relation extraction with attentive capsule network"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "a full end to end semantic role labeler syntax agnostic over syntax aware"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "deep contextualized word representations"
    - "information aggregation via dynamic routing for sequence encoding"

Connected through hyperedge HE-7 (induced by source paper "syntax aware multilingual semantic role labeling"):
  with members:
    - "syntax aware neural semantic role labeling with supertags"
    - "dependency or span end to end uniform semantic role labeling"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "deep contextualized word representations"
    - "learning word vectors for 157 languages"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "linguistically informed self attention for semantic role labeling"

Connected through hyperedge HE-8 (induced by source paper "supervised contextual embeddings for transfer learning in natural language processing tasks"):
  with members:
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "linguistically informed self attention for semantic role labeling"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "a minimal span based neural constituency parser"
    - "learned in translation contextualized word vectors"
    - "semi supervised sequence tagging with bidirectional language models"
    - "neural skill transfer from supervised language tasks to reading comprehension"
    - "encoding sentences with graph convolutional networks for semantic role labeling"

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)