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: "do transformer attention heads provide transparency in abstractive summarization"
  Abstract: Learning algorithms become more powerful, often at the cost of increased complexity. In response, the demand for algorithms to be transparent is growing. In NLP tasks, attention distributions learned by attention-based deep learning models are used to gain insights in the models' behavior. To which extent is this perspective valid for all NLP tasks? We investigate whether distributions calculated by different attention heads in a transformer architecture can be used to improve transparency in the task of abstractive summarization. To this end, we present both a qualitative and quantitative analysis to investigate the behavior of the attention heads. We show that some attention heads indeed specialize towards syntactically and semantically distinct input. We propose an approach to evaluate to which extent the Transformer model relies on specifically learned attention distributions. We also discuss what this implies for using attention distributions as a means of transparency.

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 "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"
    - "analyzing the structure of attention in a transformer language model"
    - "is attention interpretable"
    - "attention is not explanation"
    - "attention interpretability across nlp tasks"
    - "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"
    - "analyzing the structure of attention in a transformer language model"
    - "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"
    - "explaining recurrent neural network predictions in sentiment analysis"

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)