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 "concept whitening for interpretable image recognition"):
  Abstract: What does a neural network encode about a concept as we traverse through the layers? Interpretability in machine learning is undoubtedly important, but the calculations of neural networks are very challenging to understand. Attempts to see inside their hidden layers can either be misleading, unusable, or rely on the latent space to possess properties that it may not have. In this work, rather than attempting to analyze a neural network posthoc, we introduce a mechanism, called concept whitening (CW), to alter a given layer of the network to allow us to better understand the computation leading up to that layer. When a concept whitening module is added to a CNN, the axes of the latent space can be aligned with concepts of interest. By experiment, we show that CW can provide us a much clearer understanding for how the network gradually learns concepts over layers without hurting predictive performance.

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 "the convolutional tsetlin machine":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the weighted tsetlin machine compressed representations with weighted clauses")
  Member "educe explaining model decisions through unsupervised concepts extraction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on completeness aware concept based explanations in deep neural networks")
  Member "saccader improving accuracy of hard attention models for vision":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "active perception and representation for robotic manipulation")
  Member "cheap orthogonal constraints in neural networks a simple parametrization of the orthogonal and unitary group":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient riemannian optimization on the stiefel manifold via the cayley transform")
  Member "on concept based explanations in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial tcav robust and effective interpretation of intermediate layers in neural networks")
  Member "iterative normalization beyond standardization towards efficient whitening":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "regularizing activations in neural networks via distribution matching with the wasserstein metric")
  Member "this looks like that deep learning for interpretable image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "bayes probe distribution guided sampling for prediction level sets")

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.LG(Machine Learning)