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 "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"):
  Abstract: Sound event detection (SED) entails two subtasks: recognizing what types of sound events are present in an audio stream (audio tagging), and pinpointing their onset and offset times (localization). In the popular multiple instance learning (MIL) framework for SED with weak labeling, an important component is the pooling function. This paper compares five types of pooling functions both theoretically and experimentally, with special focus on their performance of localization. Although the attention pooling function is currently receiving the most attention, we find the linear softmax pooling function to perform the best among the five. Using this pooling function, we build a neural network called TALNet. It is the first system to reach state-of-the-art audio tagging performance on Audio Set, while exhibiting strong localization performance on the DCASE 2017 challenge at the same time.

Includes members:
  Member "a closer look at weak label learning for audio events":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "limitations of weak labels for embedding and tagging")
  Member "comparing the max and noisy or pooling functions in multiple instance learning for weakly supervised sequence learning tasks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "specialized decision surface and disentangled feature for weakly supervised polyphonic sound event detection")
  Member "adaptive pooling operators for weakly labeled sound event detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "limitations of weak labels for embedding and tagging")
  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 "audio set classification with attention model a probabilistic perspective":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning multi instrument classification with partial labels")
  Member "reducing model complexity for dnn based large scale audio classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "machine learning for music genre multifaceted review and experimentation with audioset")
  Member "sound event detection in multichannel audio using spatial and harmonic features":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "non negative matrix factorization convolutional neural network nmf cnn for sound event detection")
  Member "large scale weakly supervised audio classification using gated convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "source separation with weakly labelled data an approach to computational auditory scene analysis")

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)