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 "learning sound event classifiers from web audio with noisy labels"):
  Abstract: As sound event classification moves towards larger datasets, issues of label noise become inevitable. Web sites can supply large volumes of user-contributed audio and metadata, but inferring labels from this metadata introduces errors due to unreliable inputs, and limitations in the mapping. There is, however, little research into the impact of these errors. To foster the investigation of label noise in sound event classification we present FSDnoisy18k, a dataset containing 42.5 hours of audio across 20 sound classes, including a small amount of manually-labeled data and a larger quantity of real-world noisy data. We characterize the label noise empirically, and provide a CNN baseline system. Experiments suggest that training with large amounts of noisy data can outperform training with smaller amounts of carefully-labeled data. We also show that noise-robust loss functions can be effective in improving performance in presence of corrupted labels.

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 "a closer look at weak label learning for audio events":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "limitations of weak labels for embedding and tagging")
  Member "joint optimization framework for learning with noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "generalized cross entropy loss for training deep neural networks with noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification")
  Member "iterative learning with open set noisy labels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dividemix learning with noisy labels as semi supervised learning")
  Member "data efficient weakly supervised learning for low resource audio event detection using deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cosine similarity penalty to discriminate sound classes in weakly supervised sound event detection")
  Member "deep convolutional neural networks and data augmentation for environmental sound classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "applying visual domain style transfer and texture synthesis techniques to audio insights and challenges")

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.SD(Sound)