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 "exact and consistent interpretation for piecewise linear neural networks a closed form solution"):
  Abstract: Strong intelligent machines powered by deep neural networks are increasingly deployed as black boxes to make decisions in risk-sensitive domains, such as finance and medical. To reduce potential risk and build trust with users, it is critical to interpret how such machines make their decisions. Existing works interpret a pre-trained neural network by analyzing hidden neurons, mimicking pre-trained models or approximating local predictions. However, these methods do not provide a guarantee on the exactness and consistency of their interpretation. In this paper, we propose an elegant closed form solution named $OpenBox$ to compute exact and consistent interpretations for the family of Piecewise Linear Neural Networks (PLNN). The major idea is to first transform a PLNN into a mathematically equivalent set of linear classifiers, then interpret each linear classifier by the features that dominate its prediction. We further apply $OpenBox$ to demonstrate the effectiveness of non-negative and sparse constraints on improving the interpretability of PLNNs. The extensive experiments on both synthetic and real world data sets clearly demonstrate the exactness and consistency of our interpretation.

Includes members:
  Member "grad cam visual explanations from deep networks via gradient based localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "smoothgrad removing noise by adding noise":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning")
  Member "interpreting deep visual representations via network dissection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning")
  Member "deep embedding forest forest based serving with deep embedding features":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "autoint automatic feature interaction learning via self attentive neural networks")
  Member "axiomatic attribution for deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial attacks and defenses on graphs a review and empirical study")
  Member "learning important features through propagating activation differences":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "importance driven deep learning system testing")
  Member "nearly tight vc dimension and pseudodimension bounds for piecewise linear neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the generalization of bayesian deep nets for multi class classification")
  Member "fashion mnist a novel image dataset for benchmarking machine learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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.CV(Computer Vision and Pattern Recognition)