human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "interrogating the explanatory power of attention in neural machine translation"):
  Abstract: Attention models have become a crucial component in neural machine translation (NMT). They are often implicitly or explicitly used to justify the model's decision in generating a specific token but it has not yet been rigorously established to what extent attention is a reliable source of information in NMT. To evaluate the explanatory power of attention for NMT, we examine the possibility of yielding the same prediction but with counterfactual attention models that modify crucial aspects of the trained attention model. Using these counterfactual attention mechanisms we assess the extent to which they still preserve the generation of function and content words in the translation process. Compared to a state of the art attention model, our counterfactual attention models produce 68% of function words and 21% of content words in our German-English dataset. Our experiments demonstrate that attention models by themselves cannot reliably explain the decisions made by a NMT model.

Includes members:
  Member "saliency driven word alignment interpretation for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "topics to avoid demoting latent confounds in text classification")
  Member "model explanations under calibration":
    also connected through other hyperedges:
      (none)
  Member "an analysis of attention over clinical notes for predictive tasks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on model stability as a function of random seed")
  Member "analyzing the structure of attention in a transformer language model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the compositionality of neural networks integrating symbolism and connectionism")
  Member "attention is not explanation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "counterfactual samples synthesizing for robust visual question answering")
  Member "do transformer attention heads provide transparency in abstractive summarization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on identifiability in transformers")
  Member "what does bert look at an analysis of bert s attention":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "interpreting recurrent and attention based neural models a case study on natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "explaining explanations axiomatic feature interactions for deep networks")

Question: Please tell me which class the source paper 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)