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: "the repeval 2017 shared task multi genre natural language inference with sentence representations"
  Abstract: This paper presents the results of the RepEval 2017 Shared Task, which evaluated neural network sentence representation learning models on the Multi-Genre Natural Language Inference corpus (MultiNLI) recently introduced by Williams et al. (2017). All of the five participating teams beat the bidirectional LSTM (BiLSTM) and continuous bag of words baselines reported in Williams et al.. The best single model used stacked BiLSTMs with residual connections to extract sentence features and reached 74.5% accuracy on the genre-matched test set. Surprisingly, the results of the competition were fairly consistent across the genre-matched and genre-mismatched test sets, and across subsets of the test data representing a variety of linguistic phenomena, suggesting that all of the submitted systems learned reasonably domain-independent representations for sentence meaning.

Connected through hyperedge HE-1 (induced by source paper "stress test evaluation of transformer based models in natural language understanding tasks"):
  with members:
    - "huggingface s transformers state of the art natural language processing"
    - "universal transformers"
    - "a multiscale visualization of attention in the transformer model"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "probing neural network comprehension of natural language arguments"
    - "roberta a robustly optimized bert pretraining approach"
    - "is bert really robust a strong baseline for natural language attack on text classification and entailment"
    - "on adversarial examples for character level neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "linspector multilingual probing tasks for word representations"):
  with members:
    - "linspector web a multilingual probing suite for word representations"
    - "a survey of cross lingual word embedding models"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "xnli evaluating cross lingual sentence representations"
    - "on the complexity and typology of inflectional morphological systems"
    - "allennlp a deep semantic natural language processing platform"
    - "unimorph 2 0 universal morphology"

Connected through hyperedge HE-3 (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-4 (induced by source paper "visuallly grounded generation of entailments from premises"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "visual entailment a novel task for fine grained image understanding"
    - "quantifying the amount of visual information used by neural caption generators"
    - "annotation artifacts in natural language inference data"
    - "object counts bringing explicit detections back into image captioning"
    - "grounded textual entailment"
    - "where to put the image in an image caption generator"
    - "foil it find one mismatch between image and language caption"

Connected through hyperedge HE-5 (induced by source paper "equate a benchmark evaluation framework for quantitative reasoning in natural language inference"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "what knowledge is needed to solve the rte5 textual entailment challenge"
    - "hypothesis only baselines in natural language inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "stress test evaluation for natural language inference"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "situating sentence embedders with nearest neighbor overlap"):
  with members:
    - "optimization of indexing based on k nearest neighbor graph for proximity search in high dimensional data"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "content based citation recommendation"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "senteval an evaluation toolkit for universal sentence representations"

Connected through hyperedge HE-7 (induced by source paper "a question entailment approach to question answering"):
  with members:
    - "semeval 2016 task 3 community question answering"
    - "semeval 2017 task 3 community question answering"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "dr bilstm dependent reading bidirectional lstm for natural language inference"
    - "neural natural language inference models enhanced with external knowledge"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "cmu livemedqa at trec 2017 liveqa a consumer health question answering system"
    - "neural question answering at bioasq 5b"

Connected through hyperedge HE-8 (induced by source paper "adversarial decomposition of text representation"):
  with members:
    - "disentangled representation learning for non parallel text style transfer"
    - "universal sentence encoder"
    - "an efficient framework for learning sentence representations"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"

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)