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 "transfer learning for music classification and regression tasks"):
  Abstract: In this paper, we present a transfer learning approach for music classification and regression tasks. We propose to use a pre-trained convnet feature, a concatenated feature vector using the activations of feature maps of multiple layers in a trained convolutional network. We show how this convnet feature can serve as general-purpose music representation. In the experiments, a convnet is trained for music tagging and then transferred to other music-related classification and regression tasks. The convnet feature outperforms the baseline MFCC feature in all the considered tasks and several previous approaches that are aggregating MFCCs as well as low- and high-level music features.

Includes members:
  Member "the effects of noisy labels on deep convolutional neural networks for music tagging":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "wasserstein adversarial regularization war on label noise")
  Member "look listen and learn":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning representations by predicting bags of visual words")
  Member "kapre on gpu audio preprocessing layers for a quick implementation of deep neural network models with keras":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "entangled watermarks as a defense against model extraction")
  Member "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multinet multi stream feature aggregation and geometric loss strategy for multi task learning")
  Member "explaining deep convolutional neural networks on music classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "applying visual domain style transfer and texture synthesis techniques to audio insights and challenges")
  Member "towards playlist generation algorithms using rnns trained on within track transitions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "representation exploration and recommendation of music playlists")
  Member "automatic tagging using deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep learning for audio signal processing")
  Member "soundnet learning sound representations from unlabeled video":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")

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)