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: "bird detection in audio a survey and a challenge"
  Abstract: Many biological monitoring projects rely on acoustic detection of birds. Despite increasingly large datasets, this detection is often manual or semi-automatic, requiring manual tuning/postprocessing. We review the state of the art in automatic bird sound detection, and identify a widespread need for tuning-free and species-agnostic approaches. We introduce new datasets and an IEEE research challenge to address this need, to make possible the development of fully automatic algorithms for bird sound detection.

Connected through hyperedge HE-1 (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"
    - "convolutional recurrent neural networks for bird audio detection"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "automatic rain and cicada chorus filtering of bird acoustic data"):
  with members:
    - "automatic large scale classification of bird sounds is strongly improved by unsupervised feature learning"
    - "estimating continuous distributions in bayesian classifiers"
    - "the orchive data mining a massive bioacoustic archive"

Connected through hyperedge HE-3 (induced by source paper "stacked convolutional and recurrent neural networks for bird audio detection"):
  with members:
    - "convolutional recurrent neural networks for bird audio detection"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "sound event detection using spatial features and convolutional recurrent neural network"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "densely connected cnns for bird audio detection"):
  with members:
    - "densely connected convolutional networks"
    - "theano a python framework for fast computation of mathematical expressions"
    - "deep residual learning for image recognition"
    - "striving for simplicity the all convolutional net"
    - "visualizing and understanding convolutional networks"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-5 (induced by source paper "convolutional recurrent neural networks for bird audio detection"):
  with members:
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "stacked convolutional and recurrent neural networks for bird audio detection"
    - "convolutional recurrent neural networks for music classification"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "deep cnn framework for audio event recognition using weakly labeled web data"):
  with members:
    - "robust audio event recognition with 1 max pooling convolutional neural networks"
    - "audio event detection using weakly labeled data"
    - "yfcc100m the new data in multimedia research"
    - "very deep convolutional networks for large scale image recognition"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "deep learning for detection of bird vocalisations"):
  with members:
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "multi label classifier chains for bird sound"

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)