human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "comparing the max and noisy or pooling functions in multiple instance learning for weakly supervised sequence learning tasks"
  Abstract: Many sequence learning tasks require the localization of certain events in sequences. Because it can be expensive to obtain strong labeling that specifies the starting and ending times of the events, modern systems are often trained with weak labeling without explicit timing information. Multiple instance learning (MIL) is a popular framework for learning from weak labeling. In a common scenario of MIL, it is necessary to choose a pooling function to aggregate the predictions for the individual steps of the sequences. In this paper, we compare the "max" and "noisy-or" pooling functions on a speech recognition task and a sound event detection task. We find that max pooling is able to localize phonemes and sound events, while noisy-or pooling fails. We provide a theoretical explanation of the different behavior of the two pooling functions on sequence learning tasks.

Connected through hyperedge HE-1 (induced by source paper "specialized decision surface and disentangled feature for weakly supervised polyphonic sound event detection"):
  with members:
    - "sound event detection using graph laplacian regularization based on event co occurrence"
    - "weakly labelled audioset tagging with attention neural networks"
    - "sonyc a system for the monitoring analysis and mitigation of urban noise pollution"
    - "large scale weakly labeled semi supervised sound event detection in domestic environments"
    - "attention based deep multiple instance learning"
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "convolutional recurrent neural networks for polyphonic sound event detection"

Connected through hyperedge HE-2 (induced by source paper "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"):
  with members:
    - "a closer look at weak label learning for audio events"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "audio set classification with attention model a probabilistic perspective"
    - "reducing model complexity for dnn based large scale audio classification"
    - "sound event detection in multichannel audio using spatial and harmonic features"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes"

Connected through hyperedge HE-3 (induced by source paper "deep learning for audio transcription on low resource datasets"):
  with members:
    - "unsupervised feature learning based on deep models for environmental audio tagging"
    - "deep cnn framework for audio event recognition using weakly labeled web data"
    - "stacked convolutional and recurrent neural networks for bird audio detection"
    - "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network"
    - "end to end learning for music audio tagging at scale"
    - "convolutional gated recurrent neural network incorporating spatial features for audio tagging"
    - "cross stitch networks for multi task learning"
    - "trace norm regularised deep multi task learning"

Connected through hyperedge HE-4 (induced by source paper "data efficient weakly supervised learning for low resource audio event detection using deep learning"):
  with members:
    - "unsupervised feature learning based on deep models for environmental audio tagging"
    - "densely connected cnns for bird audio detection"
    - "deep cnn framework for audio event recognition using weakly labeled web data"
    - "stacked convolutional and recurrent neural networks for bird audio detection"
    - "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "convolutional gated recurrent neural network incorporating spatial features for audio tagging"
    - "audio event detection using weakly labeled data"

Question: Please tell me which class the center node 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)