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 "self explaining ai as an alternative to interpretable ai"):
  Abstract: The ability to explain decisions made by AI systems is highly sought after, especially in domains where human lives are at stake such as medicine or autonomous vehicles. While it is always possible to approximate the input-output relations of deep neural networks with human-understandable rules or a post-hoc model, the discovery of the double descent phenomena suggests that no such approximation will ever map onto the actual mechanistic functioning of deep neural networks. Double descent indicates that deep neural networks typically operate by smoothly interpolating between data points rather than by extracting a few high level rules. As a result neural networks trained on complex real world data are inherently hard to interpret and prone to failure if used outside their domain of applicability (ie, for extrapolation). To show how we might be able to trust AI despite these problems, we introduce the concept of self-explaining AI. Self-explaining AIs are capable of providing a human-understandable explanation of each decision along with confidence levels for both the decision and explanation. Some difficulties to this approach along with possible solutions are sketched. Finally, we argue it is also important that AI systems warn their user when they are asked to perform outside their domain of applicability.

Includes members:
  Member "a jamming transition from under to over parametrization affects generalization in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "self adaptive training beyond empirical risk minimization")
  Member "two models of double descent for weak features":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a deep conditioning treatment of neural networks")
  Member "one explanation does not fit all a toolkit and taxonomy of ai explainability techniques":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "survlime a method for explaining machine learning survival models")
  Member "deep double descent where bigger models and more data hurt":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "identifying weights and architectures of unknown relu networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cryptanalytic extraction of neural network models")
  Member "on the in fidelity and sensitivity for explanations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "analysis of explainers of black box deep neural networks for computer vision a survey")
  Member "knowing what you know in brain segmentation using bayesian deep neural networks":
    also connected through other hyperedges:
      (none)
  Member "an interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gated dilated networks for lung nodule classification in ct scans")

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.AI(Artificial Intelligence)