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: "revealing the dark secrets of bert"
  Abstract: BERT-based architectures currently give state-of-the-art performance on many NLP tasks, but little is known about the exact mechanisms that contribute to its success. In the current work, we focus on the interpretation of self-attention, which is one of the fundamental underlying components of BERT. Using a subset of GLUE tasks and a set of handcrafted features-of-interest, we propose the methodology and carry out a qualitative and quantitative analysis of the information encoded by the individual BERT's heads. Our findings suggest that there is a limited set of attention patterns that are repeated across different heads, indicating the overall model overparametrization. While different heads consistently use the same attention patterns, they have varying impact on performance across different tasks. We show that manually disabling attention in certain heads leads to a performance improvement over the regular fine-tuned BERT models.

Connected through hyperedge HE-1 (induced by source paper "a survey on contextual embeddings"):
  with members:
    - "episodic memory in lifelong language learning"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "visualizing and measuring the geometry of bert"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "emerging cross lingual structure in pretrained language models"
    - "bert rediscovers the classical nlp pipeline"
    - "unsupervised cross lingual representation learning at scale"
    - "q bert hessian based ultra low precision quantization of bert"

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"
    - "visualizing and understanding the effectiveness of bert"

Connected through hyperedge HE-3 (induced by source paper "calibration of pre trained transformers"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "calibration of encoder decoder models for neural machine translation"
    - "simplified neural unsupervised domain adaptation"
    - "evaluating lottery tickets under distributional shifts"
    - "albert a lite bert for self supervised learning of language representations"
    - "huggingface s transformers state of the art natural language processing"
    - "how to fine tune bert for text classification"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-4 (induced by source paper "hubert untangles bert to improve transfer across nlp tasks"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "semantics aware bert for language understanding"
    - "are sixteen heads really better than one"
    - "visualizing and measuring the geometry of bert"
    - "bert rediscovers the classical nlp pipeline"
    - "multi task deep neural networks for natural language understanding"
    - "open sesame getting inside bert s linguistic knowledge"
    - "unifying question answering text classification and regression via span extraction"

Connected through hyperedge HE-5 (induced by source paper "unsupervised transfer learning via bert neuron selection"):
  with members:
    - "visualizing and measuring the geometry of bert"
    - "bert rediscovers the classical nlp pipeline"
    - "open sesame getting inside bert s linguistic knowledge"
    - "linguistic knowledge and transferability of contextual representations"
    - "meta multi task learning for sequence modeling"
    - "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "dynamic meta embeddings for improved sentence representations"

Connected through hyperedge HE-6 (induced by source paper "tinybert distilling bert for natural language understanding"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "spanbert improving pre training by representing and predicting spans"
    - "distilling task specific knowledge from bert into simple neural networks"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "patient knowledge distillation for bert model compression"
    - "what does bert look at an analysis of bert s attention"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-7 (induced by source paper "pruning a bert based question answering model"):
  with members:
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "reducing transformer depth on demand with structured dropout"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "small and practical bert models for sequence labeling"
    - "q bert hessian based ultra low precision quantization of bert"
    - "structured pruning of large language models"
    - "well read students learn better on the importance of pre training compact models"

Connected through hyperedge HE-8 (induced by source paper "what would elsa do freezing layers during transformer fine tuning"):
  with members:
    - "are sixteen heads really better than one"
    - "parameter efficient transfer learning for nlp"
    - "huggingface s transformers state of the art natural language processing"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "what does bert look at an analysis of bert s attention"
    - "roberta a robustly optimized bert pretraining approach"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"

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)