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: "no training required exploring random encoders for sentence classification"
  Abstract: We explore various methods for computing sentence representations from pre-trained word embeddings without any training, i.e., using nothing but random parameterizations. Our aim is to put sentence embeddings on more solid footing by 1) looking at how much modern sentence embeddings gain over random methods---as it turns out, surprisingly little; and by 2) providing the field with more appropriate baselines going forward---which are, as it turns out, quite strong. We also make important observations about proper experimental protocol for sentence classification evaluation, together with recommendations for future research.

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 "text classification with lexicon from preattention mechanism"):
  with members:
    - "attention please a critical review of neural attention models in natural language processing"
    - "lifelong learning for sentiment classification"
    - "topic memory networks for short text classification"
    - "ncrf an open source neural sequence labeling toolkit"
    - "deriving machine attention from human rationales"
    - "a structured self attentive sentence embedding"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-3 (induced by source paper "on the impressive performance of randomly weighted encoders in summarization tasks"):
  with members:
    - "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"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "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-4 (induced by source paper "how powerful are randomly initialized pointcloud set functions"):
  with members:
    - "weight agnostic neural networks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "foldingnet point cloud auto encoder via deep grid deformation"
    - "learning representations and generative models for 3d point clouds"
    - "instance normalization the missing ingredient for fast stylization"
    - "a powerful generative model using random weights for the deep image representation"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "echo state neural machine translation"):
  with members:
    - "reservoir topology in deep echo state networks"
    - "richness of deep echo state network dynamics"
    - "neural language priors"
    - "attention is all you need"
    - "deep echo state network deepesn a brief survey"
    - "neural machine translation of rare words with subword units"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "adversarial nli a new benchmark for natural language understanding"):
  with members:
    - "adversarial filters of dataset biases"
    - "hellaswag can a machine really finish your sentence"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "abductive commonsense reasoning"
    - "codah an adversarially authored question answer dataset for common sense"
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "multi task deep neural networks for natural language understanding"
    - "are we modeling the task or the annotator an investigation of annotator bias in natural language understanding datasets"

Connected through hyperedge HE-7 (induced by source paper "gating mechanisms for combining character and word level word representations an empirical study"):
  with members:
    - "learning universal sentence representations with mean max attention autoencoder"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "senteval an evaluation toolkit for universal sentence representations"

Connected through hyperedge HE-8 (induced by source paper "learning compressed sentence representations for on device text processing"):
  with members:
    - "improving sentence representations with multi view frameworks"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "near lossless binarization of word embeddings"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "an efficient framework for learning sentence representations"
    - "universal language model fine tuning for text classification"

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)