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: "densely connected cnns for bird audio detection"
  Abstract: Detecting bird sounds in audio recordings automatically, if accurate enough, is expected to be of great help to the research community working in bio- and ecoacoustics, interested in monitoring biodiversity based on audio field recordings. To estimate how accurate the state-of-the-art machine learning approaches are, the Bird Audio Detection challenge involving large audio datasets was recently organized. In this paper, experiments using several types of convolutional neural networks (i.e. standard CNNs, residual nets and densely connected nets) are reported in the framework of this challenge. DenseNets were the preferred solution since they were the best performing and most compact models, leading to a 88.22% area under the receiver operator curve score on the test set of the challenge (ranked 3rd/30)1. Performance gains were obtained thank to data augmentation through time and frequency shifting, model parameter averaging during training and ensemble methods using the geometric mean. On the contrary, the attempts to enlarge the training dataset with samples of the test set with automatic predictions used as pseudo-groundtruth labels consistently degraded performance.

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"
    - "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-2 (induced by source paper "data efficient weakly supervised learning for low resource audio event detection using deep learning"):
  with members:
    - "comparing the max and noisy or pooling functions in multiple instance learning for weakly supervised sequence learning tasks"
    - "unsupervised feature learning based on deep models for environmental audio tagging"
    - "deep cnn framework for audio event recognition using weakly labeled web data"
    - "stacked convolutional and recurrent neural networks for bird audio detection"
    - "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "convolutional gated recurrent neural network incorporating spatial features for audio tagging"
    - "audio event detection using weakly labeled data"

Connected through hyperedge HE-3 (induced by source paper "nips4bplus a richly annotated birdsong audio dataset"):
  with members:
    - "data efficient weakly supervised learning for low resource audio event detection using deep learning"
    - "stacked convolutional and recurrent neural networks for bird audio detection"
    - "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "audio event detection using weakly labeled data"

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)