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: "to tune or not to tune adapting pretrained representations to diverse tasks"
  Abstract: While most previous work has focused on different pretraining objectives and architectures for transfer learning, we ask how to best adapt the pretrained model to a given target task. We focus on the two most common forms of adaptation, feature extraction (where the pretrained weights are frozen), and directly fine-tuning the pretrained model. Our empirical results across diverse NLP tasks with two state-of-the-art models show that the relative performance of fine-tuning vs. feature extraction depends on the similarity of the pretraining and target tasks. We explore possible explanations for this finding and provide a set of adaptation guidelines for the NLP practitioner.

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"
    - "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"
    - "zero shot text classification with generative language models"

Connected through hyperedge HE-2 (induced by source paper "parameter space factorization for zero shot learning across tasks and languages"):
  with members:
    - "the case for bayesian deep learning"
    - "learning and evaluating general linguistic intelligence"
    - "what do language representations really represent"
    - "mlqa evaluating cross lingual extractive question answering"
    - "how multilingual is multilingual bert"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "cross lingual language model pretraining"
    - "on the cross lingual transferability of monolingual representations"

Connected through hyperedge HE-3 (induced by source paper "efficient intent detection with dual sentence encoders"):
  with members:
    - "q8bert quantized 8bit bert"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "training neural response selection for task oriented dialogue systems"
    - "pretraining methods for dialog context representation learning"
    - "a repository of conversational datasets"
    - "zero shot cross lingual dialogue systems with transferable latent variables"
    - "towards scalable multi domain conversational agents the schema guided dialogue dataset"
    - "transfer fine tuning a bert case study"

Connected through hyperedge HE-4 (induced by source paper "improving bert fine tuning via self ensemble and self distillation"):
  with members:
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "parameter efficient transfer learning for nlp"
    - "multi task deep neural networks for natural language understanding"
    - "how to fine tune bert for text classification"
    - "bert and pals projected attention layers for efficient adaptation in multi task learning"
    - "tinybert distilling bert for natural language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "unified language model pre training for natural language understanding and generation"

Connected through hyperedge HE-5 (induced by source paper "on the linguistic representational power of neural machine translation models"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "the emergence of number and syntax units in lstm language models"
    - "a hybrid morpheme word representation for machine translation of morphologically rich languages"
    - "linguistic knowledge and transferability of contextual representations"
    - "the missing ingredient in zero shot neural machine translation"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "bpe and charcnns for translation of morphology a cross lingual comparison and analysis"
    - "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"

Connected through hyperedge HE-6 (induced by source paper "linguistic knowledge and transferability of contextual representations"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "what can linguistics and deep learning contribute to each other"
    - "what do rnn language models learn about filler gap dependencies"
    - "what s going on in neural constituency parsers an analysis"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"

Connected through hyperedge HE-7 (induced by source paper "learning to few shot learn across diverse natural language classification tasks"):
  with members:
    - "parameter efficient transfer learning for nlp"
    - "bert rediscovers the classical nlp pipeline"
    - "meta learning for low resource natural language generation in task oriented dialogue systems"
    - "learning and evaluating general linguistic intelligence"
    - "multi task deep neural networks for natural language understanding"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-8 (induced by source paper "smart robust and efficient fine tuning for pre trained natural language models through principled regularized optimization"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "parameter efficient transfer learning for nlp"
    - "spanbert improving pre training by representing and predicting spans"
    - "on the variance of the adaptive learning rate and beyond"
    - "a surprisingly robust trick for the winograd schema challenge"
    - "multi task deep neural networks for natural language understanding"
    - "albert a lite bert for self supervised learning of language representations"
    - "theoretically principled trade off between robustness and accuracy"

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)