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 baseline with convolutional neural networks"
  Abstract: The Detection and Classification of Acoustic Scenes and Events (DCASE) is a well-known IEEE AASP challenge consisting of a number of audio classification and sound event detection tasks. DCASE 2018 has five tasks: 1) Acoustic scene classification, 2) General-purpose audio tagging, 3) Bird audio detection, 4) Weakly-labeled semi-supervised sound event detection and 5) Multi-channel audio tagging. In this paper, we describe our Python implementation of all of these tasks, which we have released under an open-source license. The baseline source code contains the implementation of convolutional neural networks (CNNs), including AlexNetish and VGGish -- networks originating from computer vision. We researched how the performance varies from task to task with the same configuration of neural networks. Experiments show that the deeper VGGish network performs better than AlexNetish on all tasks except Task 1, where VGGish and AlexNetish perform similarly. With the VGGish network, we achieve an accuracy of 0.680 on Task 1, a mean average precision (mAP) of 0.928 on Task 2, an area under the curve (AUC) of 0.854 on Task 3, a sound event detection F1 score of 20.8\% on Task 4 and an F1 score of 87.75\% on Task 5.

Connected through hyperedge HE-1 (induced by source paper "cure dataset ladder networks for audio event classification"):
  with members:
    - "dcase 2018 challenge surrey cross task convolutional neural network baseline"
    - "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-2 (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 surrey cross task convolutional neural network baseline"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-3 (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 surrey cross task convolutional neural network baseline"
    - "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-4 (induced by source paper "sound event detection of weakly labelled data with cnn transformer and automatic threshold optimization"):
  with members:
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "weakly labelled audioset tagging with attention neural networks"
    - "large scale weakly labeled semi supervised sound event detection in domestic environments"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "attention is all you need"
    - "sound event detection using spatial features and convolutional recurrent neural network"
    - "mixup beyond empirical risk minimization"

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 surrey cross task convolutional neural network baseline"
    - "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 "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"):
  with members:
    - "learning sound event classifiers from web audio with noisy labels"
    - "sound event localization and detection of overlapping sources using convolutional recurrent neural networks"
    - "audio tagging with noisy labels and minimal supervision"
    - "sonyc a system for the monitoring analysis and mitigation of urban noise pollution"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "automatic tagging using deep convolutional neural networks"

Connected through hyperedge HE-7 (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 surrey cross task convolutional neural network baseline"
    - "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)