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 "adafilter adaptive filter fine tuning for deep transfer learning"):
  Abstract: There is an increasing number of pre-trained deep neural network models. However, it is still unclear how to effectively use these models for a new task. Transfer learning, which aims to transfer knowledge from source tasks to a target task, is an effective solution to this problem. Fine-tuning is a popular transfer learning technique for deep neural networks where a few rounds of training are applied to the parameters of a pre-trained model to adapt them to a new task. Despite its popularity, in this paper, we show that fine-tuning suffers from several drawbacks. We propose an adaptive fine-tuning approach, called AdaFilter, which selects only a part of the convolutional filters in the pre-trained model to optimize on a per-example basis. We use a recurrent gated network to selectively fine-tune convolutional filters based on the activations of the previous layer. We experiment with 7 public image classification datasets and the results show that AdaFilter can reduce the average classification error of the standard fine-tuning by 2.54%.

Includes members:
  Member "depthwise convolution is all you need for learning multiple visual domains":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "extd extremely tiny face detector via iterative filter reuse")
  Member "transferable end to end aspect based sentiment analysis with selective adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "graph few shot learning via knowledge transfer")
  Member "continual lifelong learning with neural networks a review":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "do better imagenet models transfer better":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "a survey on methods and theories of quantized neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "shifted and squeezed 8 bit floating point format for low precision training of deep neural networks")
  Member "spottune transfer learning through adaptive fine tuning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "agriculture vision a large aerial image database for agricultural pattern analysis")
  Member "co regularized alignment for unsupervised domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "harmonizing transferability and discriminability for adapting object detectors")
  Member "explicit inductive bias for transfer learning with convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "freeze discriminator a simple baseline for fine tuning gans")

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)