human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "deep convolutional neural networks and data augmentation for acoustic event detection"
  Abstract: We propose a novel method for Acoustic Event Detection (AED). In contrast to speech, sounds coming from acoustic events may be produced by a wide variety of sources. Furthermore, distinguishing them often requires analyzing an extended time period due to the lack of a clear sub-word unit. In order to incorporate the long-time frequency structure for AED, we introduce a convolutional neural network (CNN) with a large input field. In contrast to previous works, this enables to train audio event detection end-to-end. Our architecture is inspired by the success of VGGNet and uses small, 3x3 convolutions, but more depth than previous methods in AED. In order to prevent over-fitting and to take full advantage of the modeling capabilities of our network, we further propose a novel data augmentation method to introduce data variation. Experimental results show that our CNN significantly outperforms state of the art methods including Bag of Audio Words (BoAW) and classical CNNs, achieving a 16% absolute improvement.

Connected through hyperedge HE-1 (induced by source paper "meta learning extractors for music source separation"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "audio query based music source separation"
    - "on the variance of the adaptive learning rate and beyond"
    - "recursive speech separation for unknown number of speakers"
    - "lookahead optimizer k steps forward 1 step back"
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "meta learning a dynamical language model"
    - "wave u net a multi scale neural network for end to end audio source separation"

Connected through hyperedge HE-2 (induced by source paper "watch listen and tell multi modal weakly supervised dense event captioning"):
  with members:
    - "an attempt towards interpretable audio visual video captioning"
    - "learning to localize sound source in visual scenes"
    - "indoor sound source localization with probabilistic neural network"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "weakly supervised dense event captioning in videos"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "end to end dense video captioning with masked transformer"
    - "jointly localizing and describing events for dense video captioning"

Connected through hyperedge HE-3 (induced by source paper "attention based convolutional recurrent neural network for environmental sound classification"):
  with members:
    - "multi stream network with temporal attention for environmental sound classification"
    - "learning environmental sounds with multi scale convolutional neural network"
    - "deep convolutional neural network with mixup for environmental sound classification"
    - "learning from between class examples for deep sound recognition"
    - "mixup beyond empirical risk minimization"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "soundnet learning sound representations from unlabeled video"
    - "acoustic scene classification classifying environments from the sounds they produce"

Connected through hyperedge HE-4 (induced by source paper "data augmentation approaches for improving animal audio classification"):
  with members:
    - "deep learning a critical appraisal"
    - "audio spectrogram representations for processing with convolutional neural networks"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "aenet learning deep audio features for video analysis"
    - "multi label music genre classification from audio text and images using deep features"
    - "places an image database for deep scene understanding"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "acoustic scene classification using multi layer temporal pooling based on convolutional neural network"):
  with members:
    - "a compact and discriminative feature based on auditory summary statistics for acoustic scene classification"
    - "learning audio sequence representations for acoustic event classification"
    - "rank pooling for action recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "data augmentation via dependency tree morphing for low resource languages"):
  with members:
    - "data augmentation for low resource neural machine translation"
    - "data recombination for neural semantic parsing"
    - "character level convolutional networks for text classification"
    - "finding function in form compositional character models for open vocabulary word representation"

Connected through hyperedge HE-7 (induced by source paper "ai for earth rainforest conservation by acoustic surveillance"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "enabling early audio event detection with neural networks"):
  with members:
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "deep cnn framework for audio event recognition using weakly labeled web data"
    - "dnn and cnn with weighted and multi task loss functions for audio event detection"
    - "what makes audio event detection harder than classification"
    - "an overview of multi task learning in deep neural networks"
    - "robust audio event recognition with 1 max pooling convolutional neural networks"
    - "car forest joint classification regression decision forests for overlapping audio event detection"
    - "recurrent neural networks for polyphonic sound event detection in real life recordings"

Question: Please tell me which class the center node 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)