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 "adaptive pooling operators for weakly labeled sound event detection"):
  Abstract: Sound event detection (SED) methods are tasked with labeling segments of audio recordings by the presence of active sound sources. SED is typically posed as a supervised machine learning problem, requiring strong annotations for the presence or absence of each sound source at every time instant within the recording. However, strong annotations of this type are both labor- and cost-intensive for human annotators to produce, which limits the practical scalability of SED methods. #R##N#In this work, we treat SED as a multiple instance learning (MIL) problem, where training labels are static over a short excerpt, indicating the presence or absence of sound sources but not their temporal locality. The models, however, must still produce temporally dynamic predictions, which must be aggregated (pooled) when comparing against static labels during training. To facilitate this aggregation, we develop a family of adaptive pooling operators---referred to as auto-pool---which smoothly interpolate between common pooling operators, such as min-, max-, or average-pooling, and automatically adapt to the characteristics of the sound sources in question. We evaluate the proposed pooling operators on three datasets, and demonstrate that in each case, the proposed methods outperform non-adaptive pooling operators for static prediction, and nearly match the performance of models trained with strong, dynamic annotations. The proposed method is evaluated in conjunction with convolutional neural networks, but can be readily applied to any differentiable model for time-series label prediction.

Includes members:
  Member "sonyc a system for the monitoring analysis and mitigation of urban noise pollution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "specialized decision surface and disentangled feature for weakly supervised polyphonic sound event detection")
  Member "convolutional recurrent neural networks for polyphonic sound event detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sound event detection with depthwise separable and dilated convolutions")
  Member "a joint separation classification model for sound event detection of weakly labelled data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "non negative matrix factorization convolutional neural network nmf cnn for sound event detection")
  Member "sound event detection using spatial features and convolutional recurrent neural network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sound event detection of weakly labelled data with cnn transformer and automatic threshold optimization")
  Member "look listen and learn":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning representations by predicting bags of visual words")
  Member "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sound event detection and time frequency segmentation from weakly labelled data")
  Member "large scale weakly supervised audio classification using gated convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "source separation with weakly labelled data an approach to computational auditory scene analysis")
  Member "knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cure dataset ladder networks for audio event classification")

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)