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 "general audio tagging with ensembling convolutional neural networks and statistical features"):
  Abstract: Audio tagging aims to infer descriptive labels from audio clips and it is challenging due to the limited size of data and noisy labels. The solution to the tagging task is described in this paper. The main contributions include the following: an ensemble learning framework is applied to ensemble statistical features and the outputs from the deep classifiers, with the goal to utilize complementary information. Moreover, a sample re-weight strategy is employed to address the noisy label problem within the framework. The approach achieves a mean average precision of 0.958, outperforming the baseline system with a large margin.Audio tagging aims to infer descriptive labels from audio clips and it is challenging due to the limited size of data and noisy labels. The solution to the tagging task is described in this paper. The main contributions include the following: an ensemble learning framework is applied to ensemble statistical features and the outputs from the deep classifiers, with the goal to utilize complementary information. Moreover, a sample re-weight strategy is employed to address the noisy label problem within the framework. The approach achieves a mean average precision of 0.958, outperforming the baseline system with a large margin.

Includes members:
  Member "general purpose tagging of freesound audio with audioset labels task description dataset and baseline":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi representation knowledge distillation for audio classification")
  Member "a simple fusion of deep and shallow learning for acoustic scene classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "model agnostic approaches to handling noisy labels when training sound event classifiers")
  Member "sample dropout for audio scene classification using multi scale dense connected convolutional neural network":
    also connected through other hyperedges:
      (none)
  Member "mixup based acoustic scene classification using multi channel convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mixup regularization for region proposal based object detectors")
  Member "unsupervised feature learning based on deep models for environmental audio tagging":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "limitations of weak labels for embedding and tagging")
  Member "mixup beyond empirical risk minimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on feature normalization and data augmentation")
  Member "squeeze and excitation networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "dual path networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "convolutional networks with dense connectivity")

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)