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 "layer wise pruning and auto tuning of layer wise learning rates in fine tuning of deep networks"):
  Abstract: Existing fine-tuning methods use a single learning rate over all layers. In this paper, first, we discuss that trends of layer-wise weight variations by fine-tuning using a single learning rate do not match the well-known notion that lower-level layers extract general features and higher-level layers extract specific features. Based on our discussion, we propose an algorithm that improves fine-tuning performance and reduces network complexity through layer-wise pruning and auto-tuning of layer-wise learning rates. Through in-depth experiments on image retrieval (CUB-200-2011, Stanford online products, and Inshop) and fine-grained classification (Stanford cars, Aircraft) datasets, the effectiveness of the proposed algorithm is verified.

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 "backbone can not be trained at once rolling back to pre trained network for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "push for center learning via orthogonalization and subspace masking for person re identification")
  Member "multi similarity loss with general pair weighting for deep metric learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")
  Member "large scale fine grained categorization and domain specific transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rethinking the hyperparameters for fine tuning")
  Member "do better imagenet models transfer better":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "classification is a strong baseline for deep metric learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a metric learning reality check")
  Member "learning to navigate for fine grained classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pay attention to the activations a modular attention mechanism for fine grained image recognition")
  Member "deep randomized ensembles for metric learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")

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)