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: "stacked convolutional and recurrent neural networks for bird audio detection"
  Abstract: This paper studies the detection of bird calls in audio segments using stacked convolutional and recurrent neural networks. Data augmentation by blocks mixing and domain adaptation using a novel method of test mixing are proposed and evaluated in regard to making the method robust to unseen data. The contributions of two kinds of acoustic features (dominant frequency and log mel-band energy) and their combinations are studied in the context of bird audio detection. Our best achieved AUC measure on five cross-validations of the development data is 95.5% and 88.1% on the unseen evaluation data.

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 bird audio detection"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "deep learning for audio transcription on low resource datasets"):
  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"
    - "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network"
    - "end to end learning for music audio tagging at scale"
    - "convolutional gated recurrent neural network incorporating spatial features for audio tagging"
    - "cross stitch networks for multi task learning"
    - "trace norm regularised deep multi task learning"

Connected through hyperedge HE-3 (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"
    - "densely connected cnns for bird audio detection"
    - "deep cnn framework for audio event recognition using weakly labeled web data"
    - "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-4 (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"
    - "densely connected cnns 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"

Connected through hyperedge HE-5 (induced by source paper "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network"):
  with members:
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "deep cnn framework for audio event recognition using weakly labeled web data"
    - "sound event detection using spatial features and convolutional recurrent neural network"
    - "convolutional gated recurrent neural network incorporating spatial features for audio tagging"
    - "audio event detection using weakly labeled data"
    - "theano a python framework for fast computation of mathematical expressions"
    - "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 "convolutional recurrent neural networks for bird audio detection"):
  with members:
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "bird detection in audio a survey and a challenge"
    - "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)