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 "deep learning of human perception in audio event classification"):
  Abstract: In this paper, we introduce our recent studies on human perception in audio event classification by different deep learning models. In particular, the pre-trained model VGGish is used as feature extractor to process audio data, and DenseNet is trained by and used as feature extractor for our electroencephalography (EEG) data. The correlation between audio stimuli and EEG is learned in a shared space. In the experiments, we record brain activities (EEG signals) of several subjects while they are listening to music events of 8 audio categories selected from Google AudioSet, using a 16-channel EEG headset with active electrodes. Our experimental results demonstrate that i) audio event classification can be improved by exploiting the power of human perception, and ii) the correlation between audio stimuli and EEG can be learned to complement audio event understanding.

Includes members:
  Member "category based deep cca for fine grained venue discovery from multimodal data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "video face super resolution with motion adaptive feedback cell")
  Member "cnn architectures for large scale audio classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi modal dense video captioning")
  Member "densenet implementing efficient convnet descriptor pyramids":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fdftnet facing off fake images using fake detection fine tuning network")
  Member "fully convolutional networks for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gan compression efficient architectures for interactive conditional 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.MM(Multimedia)