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: "senteval an evaluation toolkit for universal sentence representations"
  Abstract: We introduce SentEval, a toolkit for evaluating the quality of universal sentence representations. SentEval encompasses a variety of tasks, including binary and multi-class classification, natural language inference and sentence similarity. The set of tasks was selected based on what appears to be the community consensus regarding the appropriate evaluations for universal sentence representations. The toolkit comes with scripts to download and preprocess datasets, and an easy interface to evaluate sentence encoders. The aim is to provide a fairer, less cumbersome and more centralized way for evaluating sentence representations.

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"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "learning to few shot learn across diverse natural language classification tasks"

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"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "bert of theseus compressing bert by progressive module replacing"):
  with members:
    - "are sixteen heads really better than one"
    - "reducing transformer depth on demand with structured dropout"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "q bert hessian based ultra low precision quantization of bert"
    - "well read students learn better on the importance of pre training compact models"
    - "deep double descent where bigger models and more data hurt"
    - "multilingual neural machine translation with knowledge distillation"
    - "mass masked sequence to sequence pre training for language generation"

Connected through hyperedge HE-4 (induced by source paper "jiant a software toolkit for research on general purpose text understanding models"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "bert rediscovers the classical nlp pipeline"
    - "multi task deep neural networks for natural language understanding"
    - "open sesame getting inside bert s linguistic knowledge"
    - "albert a lite bert for self supervised learning of language representations"
    - "probing what different nlp tasks teach machines about function word comprehension"

Connected through hyperedge HE-5 (induced by source paper "incorporating visual semantics into sentence representations within a grounded space"):
  with members:
    - "sentence embedding alignment for lifelong relation extraction"
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "deep contextualized word representations"
    - "an efficient framework for learning sentence representations"
    - "learning visually grounded sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "unsupervised sentence representations as word information series revisiting tf idf"

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"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "on the impressive performance of randomly weighted encoders in summarization tasks"):
  with members:
    - "no training required exploring random encoders for sentence classification"
    - "syntax helps elmo understand semantics is syntax still relevant in a deep neural architecture for srl"
    - "a discourse aware attention model for abstractive summarization of long documents"
    - "learning visually grounded sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "still not systematic after all these years on the compositional skills of sequence to sequence recurrent networks"
    - "get to the point summarization with pointer generator networks"
    - "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"

Connected through hyperedge HE-8 (induced by source paper "embedding compression with isotropic iterative quantization"):
  with members:
    - "all but the top simple and effective postprocessing for word representations"
    - "compressing word embeddings via deep compositional code learning"
    - "bag of tricks for efficient text classification"
    - "neural architectures for named entity recognition"
    - "binary paragraph vectors"
    - "compression of neural machine translation models via pruning"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "a latent variable model approach to pmi based word embeddings"

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)