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: "sentence bert sentence embeddings using siamese bert networks"
  Abstract: BERT (Devlin et al., 2018) and RoBERTa (Liu et al., 2019) has set a new state-of-the-art performance on sentence-pair regression tasks like semantic textual similarity (STS). However, it requires that both sentences are fed into the network, which causes a massive computational overhead: Finding the most similar pair in a collection of 10,000 sentences requires about 50 million inference computations (~65 hours) with BERT. The construction of BERT makes it unsuitable for semantic similarity search as well as for unsupervised tasks like clustering. #R##N#In this publication, we present Sentence-BERT (SBERT), a modification of the pretrained BERT network that use siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared using cosine-similarity. This reduces the effort for finding the most similar pair from 65 hours with BERT / RoBERTa to about 5 seconds with SBERT, while maintaining the accuracy from BERT. #R##N#We evaluate SBERT and SRoBERTa on common STS tasks and transfer learning tasks, where it outperforms other state-of-the-art sentence embeddings methods.

Connected through hyperedge HE-1 (induced by source paper "contextual lensing of universal sentence representations"):
  with members:
    - "insertion transformer flexible sequence generation via insertion operations"
    - "ccmatrix mining billions of high quality parallel sentences on the web"
    - "parameter efficient transfer learning for nlp"
    - "unsupervised cross lingual representation learning at scale"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "learning to few shot learn across diverse natural language classification tasks"
    - "zero shot text classification with generative language models"

Connected through hyperedge HE-2 (induced by source paper "sbert wk a sentence embedding method by dissecting bert based word models"):
  with members:
    - "are sixteen heads really better than one"
    - "language models as knowledge bases"
    - "pitfalls in the evaluation of sentence embeddings"
    - "evaluating word embedding models methods and experimental results"
    - "albert a lite bert for self supervised learning of language representations"
    - "how contextual are contextualized word representations comparing the geometry of bert elmo and gpt 2 embeddings"
    - "revealing the dark secrets of bert"
    - "visualizing and understanding the effectiveness of bert"

Connected through hyperedge HE-3 (induced by source paper "sentence analogies exploring linguistic relationships and regularities in sentence embeddings"):
  with members:
    - "a primer in bertology what we know about how bert works"
    - "efficient sentence embedding using discrete cosine transform"
    - "transformer xl attentive language models beyond a fixed length context"
    - "fair is better than sensational man is to doctor as woman is to doctor"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "bart denoising sequence to sequence pre training for natural language generation translation and comprehension"
    - "assessing composition in sentence vector representations"

Connected through hyperedge HE-4 (induced by source paper "multi level head wise match and aggregation in transformer for textual sequence matching"):
  with members:
    - "multi step retriever reader interaction for scalable open domain question answering"
    - "phrase indexed question answering a new challenge for scalable document comprehension"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "monotonic cardinality estimation of similarity selection a deep learning approach"):
  with members:
    - "neo a learned query optimizer"
    - "selectivity estimation with deep likelihood models"
    - "an empirical analysis of deep learning for cardinality estimation"
    - "multi attribute selectivity estimation using deep learning"
    - "an end to end learning based cost estimator"
    - "learned cardinalities estimating correlated joins with deep learning"
    - "learning state representations for query optimization with deep reinforcement learning"
    - "deep reinforcement learning for join order enumeration"

Connected through hyperedge HE-6 (induced by source paper "efficient sentence embedding via semantic subspace analysis"):
  with members:
    - "sbert wk a sentence embedding method by dissecting bert based word models"
    - "evaluating word embedding models methods and experimental results"
    - "vector of locally aggregated word embeddings vlawe a novel document level representation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "post processing of word representations via variance normalization and dynamic embedding"

Connected through hyperedge HE-7 (induced by source paper "event sequence metric learning"):
  with members:
    - "e t rnn applying deep learning to credit loan applications"
    - "metric learning for adversarial robustness"
    - "learning a unified embedding for visual search at pinterest"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "a comparison study of credit card fraud detection supervised versus unsupervised"
    - "prediction is very hard especially about conversion predicting user purchases from clickstream data in fashion e commerce"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-8 (induced by source paper "hierarchical transformer network for utterance level emotion recognition"):
  with members:
    - "pt code pre trained context dependent encoder for utterance level emotion recognition"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "transformer xl attentive language models beyond a fixed length context"
    - "universal transformers"
    - "knowledge enriched transformer for emotion detection in textual conversations"
    - "higru hierarchical gated recurrent units for utterance level emotion recognition"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)