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: "analyzing the structure of attention in a transformer language model"
  Abstract: The Transformer is a fully attention-based alternative to recurrent networks that has achieved state-of-the-art results across a range of NLP tasks. In this paper, we analyze the structure of attention in a Transformer language model, the GPT-2 small pretrained model. We visualize attention for individual instances and analyze the interaction between attention and syntax over a large corpus. We find that attention targets different parts of speech at different layer depths within the model, and that attention aligns with dependency relations most strongly in the middle layers. We also find that the deepest layers of the model capture the most distant relationships. Finally, we extract exemplar sentences that reveal highly specific patterns targeted by particular attention heads.

Connected through hyperedge HE-1 (induced by source paper "the compositionality of neural networks integrating symbolism and connectionism"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "cnns found to jump around more skillfully than rnns compositional generalization in seq2seq convolutional networks"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "measuring compositionality in representation learning"
    - "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"
    - "open sesame getting inside bert s linguistic knowledge"

Connected through hyperedge HE-2 (induced by source paper "understanding multi head attention in abstractive summarization"):
  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"
    - "adaptively sparse transformers"
    - "is attention interpretable"
    - "attention is not explanation"
    - "attention interpretability across nlp tasks"
    - "do transformer attention heads provide transparency in abstractive summarization"
    - "what does bert look at an analysis of bert s attention"

Connected through hyperedge HE-3 (induced by source paper "interrogating the explanatory power of attention in neural machine translation"):
  with members:
    - "saliency driven word alignment interpretation for neural machine translation"
    - "model explanations under calibration"
    - "an analysis of attention over clinical notes for predictive tasks"
    - "attention is not explanation"
    - "do transformer attention heads provide transparency in abstractive summarization"
    - "what does bert look at an analysis of bert s attention"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "explaining recurrent neural network predictions in sentiment analysis"

Connected through hyperedge HE-4 (induced by source paper "sanvis visual analytics for understanding self attention networks"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "gan lab understanding complex deep generative models using interactive visual experimentation"
    - "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "what does bert look at an analysis of bert s attention"
    - "deeptracker visualizing the training process of convolutional neural networks"
    - "do convolutional neural networks learn class hierarchy"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "attention interpretability across nlp tasks"):
  with members:
    - "attention is not not explanation"
    - "bert rediscovers the classical nlp pipeline"
    - "is attention interpretable"
    - "attention is not explanation"
    - "what does bert look at an analysis of bert s attention"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning structured text representations"

Connected through hyperedge HE-6 (induced by source paper "are transformers universal approximators of sequence to sequence functions"):
  with members:
    - "visualizing and measuring the geometry of bert"
    - "universal approximations of permutation invariant equivariant functions by deep neural networks"
    - "pay less attention with lightweight and dynamic convolutions"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "what does bert look at an analysis of bert s attention"
    - "on the turing completeness of modern neural network architectures"
    - "roberta a robustly optimized bert pretraining approach"
    - "resnet with one neuron hidden layers is a universal approximator"

Connected through hyperedge HE-7 (induced by source paper "exbert a visual analysis tool to explore learned representations in transformers models"):
  with members:
    - "attention is not not explanation"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "bert rediscovers the classical nlp pipeline"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "ctrl a conditional transformer language model for controllable generation"
    - "attention is not explanation"
    - "a multiscale visualization of attention in the transformer model"
    - "what does bert look at an analysis of bert s attention"

Connected through hyperedge HE-8 (induced by source paper "a multiscale visualization of attention in the transformer model"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "attention is not explanation"
    - "gender bias in neural natural language processing"
    - "analysis methods in neural language processing a survey"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "gender bias in coreference resolution evaluation and debiasing methods"
    - "attention is all you need"
    - "a neural attention model for abstractive sentence summarization"

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)