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: "i know what you want semantic learning for text comprehension"
  Abstract: Who did what to whom is a major focus in natural language understanding, which is right the aim of semantic role labeling (SRL). Although SRL is naturally essential to text comprehension tasks, it is surprisingly ignored in previous work. This paper thus makes the first attempt to let SRL enhance text comprehension and inference through specifying verbal arguments and their corresponding semantic roles. In terms of deep learning models, our embeddings are enhanced by semantic role labels for more fine-grained semantics. We show that the salient labels can be conveniently added to existing models and significantly improve deep learning models in challenging text comprehension tasks. Extensive experiments on benchmark machine reading comprehension and inference datasets verify that the proposed semantic learning helps our system reach new state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "semantics aware bert for language understanding"):
  with members:
    - "limit bert linguistic informed multi task bert"
    - "dependency or span end to end uniform semantic role labeling"
    - "ernie enhanced representation through knowledge integration"
    - "spanbert improving pre training by representing and predicting spans"
    - "sg net syntax guided machine reading comprehension"
    - "multi task deep neural networks for natural language understanding"
    - "ernie enhanced language representation with informative entities"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-2 (induced by source paper "infusing knowledge into the textual entailment task using graph convolutional networks"):
  with members:
    - "several experiments on investigating pretraining and knowledge enhanced models for natural language inference"
    - "multi task deep neural networks for natural language understanding"
    - "sentence embeddings in nli with iterative refinement encoders"
    - "roberta a robustly optimized bert pretraining approach"
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "hypothesis only baselines in natural language inference"
    - "improving natural language inference using external knowledge in the science questions domain"

Connected through hyperedge HE-3 (induced by source paper "limit bert linguistic informed multi task bert"):
  with members:
    - "semantics aware bert for language understanding"
    - "dependency or span end to end uniform semantic role labeling"
    - "parsing all syntax and semantics dependencies and spans"
    - "multi task deep neural networks for natural language understanding"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "what s going on in neural constituency parsers an analysis"
    - "multilingual constituency parsing with self attention and pre training"
    - "jointly predicting predicates and arguments in neural semantic role labeling"

Connected through hyperedge HE-4 (induced by source paper "dependency or span end to end uniform semantic role labeling"):
  with members:
    - "learning joint semantic parsers from disjoint data"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"
    - "end to end neural coreference resolution"
    - "deep semantic role labeling with self attention"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "neural semantic role labeling with dependency path embeddings"

Connected through hyperedge HE-5 (induced by source paper "path based contextualization of knowledge graphs for textual entailment"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "improving natural language inference using external knowledge in the science questions domain"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural natural language inference models enhanced with external knowledge"
    - "a decomposable attention model for natural language inference"
    - "a hierarchical neural autoencoder for paragraphs and documents"

Connected through hyperedge HE-6 (induced by source paper "integrate image representation to text model on sentence level a semi supervised framework"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "discourse marker augmented network with reinforcement learning for natural language inference"
    - "yuanfudao at semeval 2018 task 11 three way attention and relational knowledge for commonsense machine comprehension"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning visually grounded sentence representations"
    - "vse improving visual semantic embeddings with hard negatives"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "effective subword segmentation for text comprehension"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "discourse marker augmented network with reinforcement learning for natural language inference"
    - "dual co matching network for multi choice reading comprehension"
    - "graph based bilingual word embedding for statistical machine translation"
    - "open vocabulary learning for neural chinese pinyin ime"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"
    - "one shot learning for question answering in gaokao history challenge"

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)