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: "dnn and cnn with weighted and multi task loss functions for audio event detection"
  Abstract: This report presents our audio event detection system submitted for Task 2, "Detection of rare sound events", of DCASE 2017 challenge. The proposed system is based on convolutional neural networks (CNNs) and deep neural networks (DNNs) coupled with novel weighted and multi-task loss functions and state-of-the-art phase-aware signal enhancement. The loss functions are tailored for audio event detection in audio streams. The weighted loss is designed to tackle the common issue of imbalanced data in background/foreground classification while the multi-task loss enables the networks to simultaneously model the class distribution and the temporal structures of the target events for recognition. Our proposed systems significantly outperform the challenge baseline, improving F-score from 72.7% to 90.0% and reducing detection error rate from 0.53 to 0.18 on average on the development data. On the evaluation data, our submission obtains an average F1-score of 88.3% and an error rate of 0.22 which are significantly better than those obtained by the DCASE baseline (i.e. an F1-score of 64.1% and an error rate of 0.64).

Connected through hyperedge HE-1 (induced by source paper "unifying isolated and overlapping audio event detection with multi label multi task convolutional recurrent neural networks"):
  with members:
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "r crnn region based convolutional recurrent neural network for audio event detection"
    - "enabling early audio event detection with neural networks"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "deep cnn framework for audio event recognition using weakly labeled web data"
    - "what makes audio event detection harder than classification"
    - "car forest joint classification regression decision forests for overlapping audio event detection"
    - "recurrent neural networks for polyphonic sound event detection in real life recordings"

Connected through hyperedge HE-2 (induced by source paper "enabling early audio event detection with neural networks"):
  with members:
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "deep cnn framework for audio event recognition using weakly labeled web data"
    - "what makes audio event detection harder than classification"
    - "an overview of multi task learning in deep neural networks"
    - "deep convolutional neural networks and data augmentation for acoustic event detection"
    - "robust audio event recognition with 1 max pooling convolutional neural networks"
    - "car forest joint classification regression decision forests for overlapping audio event detection"
    - "recurrent neural networks for polyphonic sound event detection in real life recordings"

Connected through hyperedge HE-3 (induced by source paper "wrapped loss function for regularizing nonconforming residual distributions"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"
    - "scene parsing with multiscale feature learning purity trees and optimal covers"

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)