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: "dcase 2018 challenge surrey cross task convolutional neural network baseline"
  Abstract: The Detection and Classiﬁcation of Acoustic Scenes and Events (DCASE) consists of ﬁve audio classiﬁcation and sound event detectiontasks: 1)Acousticsceneclassiﬁcation,2)General-purposeaudio tagging of Freesound, 3) Bird audio detection, 4) Weakly-labeled semi-supervised sound event detection and 5) Multi-channel audio classiﬁcation. In this paper, we create a cross-task baseline system for all ﬁve tasks based on a convlutional neural network (CNN): a “CNN Baseline” system. We implemented CNNs with 4 layers and 8 layers originating from AlexNet and VGG from computer vision. We investigated how the performance varies from task to task with the same conﬁguration of neural networks. Experiments show that deeper CNN with 8 layers performs better than CNN with 4 layers on all tasks except Task 1. Using CNN with 8 layers, we achieve an accuracy of 0.680 on Task 1, an accuracy of 0.895 and a mean average precision (MAP) of 0.928 on Task 2, an accuracy of 0.751 andanareaunderthecurve(AUC)of0.854onTask3,asoundevent detectionF1scoreof20.8%onTask4,andanF1scoreof87.75%on Task 5. We released the Python source code of the baseline systems under the MIT license for further research.

Connected through hyperedge HE-1 (induced by source paper "robust acoustic scene classification using a multi spectrogram encoder decoder framework"):
  with members:
    - "enhancing sound texture in cnn based acoustic scene classification"
    - "acoustic scene classification by implicitly identifying distinct sound events"
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "spatio temporal attention pooling for audio scene classification"
    - "enabling early audio event detection with neural networks"
    - "a compact and discriminative feature based on auditory summary statistics for acoustic scene classification"
    - "beyond equal length snippets how long is sufficient to recognize an audio scene"
    - "mixup based acoustic scene classification using multi channel convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "cure dataset ladder networks for audio event classification"):
  with members:
    - "dcase 2018 challenge baseline with convolutional neural networks"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes"
    - "audio event detection using weakly labeled data"
    - "soundnet learning sound representations from unlabeled video"
    - "deconstructing the ladder network architecture"
    - "semi supervised learning with ladder networks"

Connected through hyperedge HE-3 (induced by source paper "hodge and podge hybrid supervised sound event detection with multi hot mixmatch and composition consistence training"):
  with members:
    - "hodgepodge sound event detection based on ensemble of semi supervised learning methods"
    - "mixmatch a holistic approach to semi supervised learning"
    - "large scale weakly labeled semi supervised sound event detection in domestic environments"
    - "dcase 2018 challenge baseline with convolutional neural networks"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-4 (induced by source paper "sound event detection and time frequency segmentation from weakly labelled data"):
  with members:
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "dcase 2018 challenge baseline with convolutional neural networks"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "unsupervised feature learning based on deep models for environmental audio tagging"
    - "multiple instance deep learning for weakly supervised small footprint audio event detection"
    - "a joint separation classification model for sound event detection of weakly labelled data"
    - "deep cnn framework for audio event recognition using weakly labeled web data"
    - "audio set classification with attention model a probabilistic perspective"

Connected through hyperedge HE-5 (induced by source paper "dcase 2019 cnn depth analysis with different channel inputs for acoustic scene classification"):
  with members:
    - "acoustic scene classification using multi layer temporal pooling based on convolutional neural network"
    - "dcase 2018 challenge baseline with convolutional neural networks"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "xception deep learning with depthwise separable convolutions"
    - "soundnet learning sound representations from unlabeled video"
    - "acoustic scene classification using convolutional neural network and multiple width frequency delta data augmentation"
    - "understanding audio pattern using convolutional neural network from raw waveforms"

Connected through hyperedge HE-6 (induced by source paper "hodgepodge sound event detection based on ensemble of semi supervised learning methods"):
  with members:
    - "mixmatch a holistic approach to semi supervised learning"
    - "large scale weakly labeled semi supervised sound event detection in domestic environments"
    - "dcase 2018 challenge baseline with convolutional neural networks"
    - "mixup beyond empirical risk minimization"

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)