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: "convolutional recurrent neural networks for bird audio detection"
  Abstract: Bird sounds possess distinctive spectral structure which may exhibit small shifts in spectrum depending on the bird species and environmental conditions. In this paper, we propose using convolutional recurrent neural networks on the task of automated bird audio detection in real-life environments. In the proposed method, convolutional layers extract high dimensional, local frequency shift invariant features, while recurrent layers capture longer term dependencies between the features extracted from short time frames. This method achieves 88.5% Area Under ROC Curve (AUC) score on the unseen evaluation data and obtains the second place in the Bird Audio Detection challenge.

Connected through hyperedge HE-1 (induced by source paper "sound event detection with depthwise separable and dilated convolutions"):
  with members:
    - "language modelling for sound event detection with teacher forcing and scheduled sampling"
    - "sound event localization and detection of overlapping sources using convolutional recurrent neural networks"
    - "network decoupling from regular to depthwise separable convolutions"
    - "listening for sirens locating and classifying acoustic alarms in city scenes"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "surrey cvssp system for dcase2017 challenge task 4"
    - "stacked convolutional and recurrent neural networks for music emotion recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-2 (induced by source paper "robust sound event detection in bioacoustic sensor networks"):
  with members:
    - "learning to detect dysarthria from raw speech"
    - "attention based end to end models for small footprint keyword spotting"
    - "recognizing birds from sound the 2018 birdclef baseline system"
    - "densely connected cnns for bird audio detection"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "deformable convolutional networks"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "reducing bias in production speech models"

Connected through hyperedge HE-3 (induced by source paper "music artist classification with convolutional recurrent neural networks"):
  with members:
    - "xgbod improving supervised outlier detection with unsupervised representation learning"
    - "lscp locally selective combination in parallel outlier ensembles"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "explaining deep convolutional neural networks on music classification"
    - "automatic tagging using deep convolutional neural networks"
    - "wavenet a generative model for raw audio"
    - "convolutional recurrent neural networks for music classification"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "speaker diarization using deep recurrent convolutional neural networks for speaker embeddings"):
  with members:
    - "tristounet triplet loss for speaker turn embedding"
    - "learning multiscale features directly from waveforms"
    - "theano a python framework for fast computation of mathematical expressions"
    - "convolutional recurrent neural networks for music classification"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep clustering discriminative embeddings for segmentation and separation"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "stacked convolutional and recurrent neural networks for bird audio detection"):
  with members:
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "sound event detection using spatial features and convolutional recurrent neural network"
    - "bird detection in audio a survey and a challenge"
    - "recurrent neural networks for polyphonic sound event detection in real life recordings"
    - "theano a python framework for fast computation of mathematical expressions"
    - "convolutional recurrent neural networks for music classification"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

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)