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 "unifying isolated and overlapping audio event detection with multi label multi task convolutional recurrent neural networks"):
  Abstract: We propose a multi-label multi-task framework based on a convolutional recurrent neural network to unify detection of isolated and overlapping audio events. The framework leverages the power of convolutional recurrent neural network architectures; convolutional layers learn effective features over which higher recurrent layers perform sequential modelling. Furthermore, the output layer is designed to handle arbitrary degrees of event overlap. At each time step in the recurrent output sequence, an output triple is dedicated to each event category of interest to jointly model event occurrence and temporal boundaries. That is, the network jointly determines whether an event of this category occurs, and when it occurs, by estimating onset and offset positions at each recurrent time step. We then introduce three sequential losses for network training: multi-label classification loss, distance estimation loss, and confidence loss. We demonstrate good generalization on two datasets: ITC-Irst for isolated audio event detection, and TUT-SED-Synthetic-2016 for overlapping audio event detection.

Includes members:
  Member "sound event detection and time frequency segmentation from weakly labelled data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "source separation with weakly labelled data an approach to computational auditory scene analysis")
  Member "r crnn region based convolutional recurrent neural network for audio event detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi scale time frequency attention for acoustic event detection")
  Member "enabling early audio event detection with neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust acoustic scene classification using a multi spectrogram encoder decoder framework")
  Member "convolutional recurrent neural networks for polyphonic sound event detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (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-6 (induced by source paper "enabling early audio event detection with neural networks")
  Member "what makes audio event detection harder than classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "enabling early audio event detection with neural networks")
  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 "enabling early audio event detection with 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.LG(Machine Learning)