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 "enabling early audio event detection with neural networks"):
  Abstract: This paper presents a methodology for early detection of audio events from audio streams. Early detection is the ability to infer an ongoing event during its initial stage. The proposed system consists of a novel inference step coupled with dual parallel tailored-loss deep neural networks (DNNs). The DNNs share a similar architecture except for their loss functions, i.e. weighted loss and multitask loss, which are designed to efficiently cope with issues common to audio event detection. The inference step is newly introduced to make use of the network outputs for recognizing ongoing events. The monotonicity of the detection function is required for reliable early detection, and will also be proved. Experiments on the ITC-Irst database show that the proposed system achieves state-of-the-art detection performance. Furthermore, even partial events are sufficient to achieve good performance similar to that obtained when an entire event is observed, enabling early event detection.

Includes members:
  Member "convolutional recurrent neural networks for polyphonic sound event detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sound event detection with depthwise separable and dilated convolutions")
  Member "deep cnn framework for audio event recognition using weakly labeled web data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sound event detection and time frequency segmentation from weakly labelled data")
  Member "dnn and cnn with weighted and multi task loss functions for audio event detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unifying isolated and overlapping audio event detection with multi label multi task convolutional recurrent neural networks")
  Member "what makes audio event detection harder than classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unifying isolated and overlapping audio event detection with multi label multi task convolutional recurrent neural networks")
  Member "an overview of multi task learning in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "deep convolutional neural networks and data augmentation for acoustic event detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (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-8 (induced by source paper "unifying isolated and overlapping audio event detection with multi label multi task convolutional recurrent neural networks")

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)