human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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).

Includes members:
  Member "deep cnn framework for audio event recognition using weakly labeled web data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sound event detection and time frequency segmentation from weakly labelled data")
  Member "an overview of multi task learning in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "learning compact structural representations for audio events using regressor banks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "what makes audio event detection harder than classification")
  Member "deep convolutional neural networks and data augmentation for acoustic event detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta learning extractors for music source separation")
  Member "robust audio event recognition with 1 max pooling convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sound event detection of weakly labelled data with cnn transformer and automatic threshold optimization")
  Member "car forest joint classification regression decision forests for overlapping audio event detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unifying isolated and overlapping audio event detection with multi label multi task convolutional recurrent neural networks")
  Member "recurrent neural networks for polyphonic sound event detection in real life recordings":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep learning for audio signal processing")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

Question: Please tell me which class the source paper 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)