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: "visualizing attention in transformer based language representation models"
  Abstract: We present an open-source tool for visualizing multi-head self-attention in Transformer-based language representation models. The tool extends earlier work by visualizing attention at three levels of granularity: the attention-head level, the model level, and the neuron level. We describe how each of these views can help to interpret the model, and we demonstrate the tool on the BERT model and the OpenAI GPT-2 model. We also present three use cases for analyzing GPT-2: detecting model bias, identifying recurring patterns, and linking neurons to model behavior.

Connected through hyperedge HE-1 (induced by source paper "on identifiability in transformers"):
  with members:
    - "attention is not not explanation"
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "attention is not all you need for commonsense reasoning"
    - "visualizing and measuring the geometry of bert"
    - "bert rediscovers the classical nlp pipeline"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "adding interpretable attention to neural translation models improves word alignment"

Connected through hyperedge HE-2 (induced by source paper "75 languages 1 model parsing universal dependencies universally"):
  with members:
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "evaluating contextualized embeddings on 54 languages in pos tagging lemmatization and dependency parsing"
    - "polyglot contextual representations improve crosslingual transfer"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "parameter sharing between dependency parsers for related languages"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"

Connected through hyperedge HE-3 (induced by source paper "what does bert look at an analysis of bert s attention"):
  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"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "attention is not explanation"
    - "linguistic knowledge and transferability of contextual representations"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"

Connected through hyperedge HE-4 (induced by source paper "allennlp interpret a framework for explaining predictions of nlp models"):
  with members:
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "roberta a robustly optimized bert pretraining approach"
    - "annotation artifacts in natural language inference data"
    - "qadiver interactive framework for diagnosing qa models"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "interpreting neural networks with nearest neighbors"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "tree transformer integrating tree structures into self attention"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "compound probabilistic context free grammars for grammar induction"
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "linguistic knowledge and transferability of contextual representations"
    - "unified language model pre training for natural language understanding and generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "linguistically informed self attention for semantic role labeling"

Connected through hyperedge HE-6 (induced by source paper "visualizing and measuring the geometry of bert"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "linguistic knowledge and transferability of contextual representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep rnns encode soft hierarchical syntax"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "attention is all you need"

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.HC(Human-Computer Interaction)