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: "convolutional recurrent neural networks for polyphonic sound event detection"
  Abstract: Sound events often occur in unstructured environments where they exhibit wide variations in their frequency content and temporal structure. Convolutional neural networks CNNs are able to extract higher level features that are invariant to local spectral and temporal variations. Recurrent neural networks RNNs are powerful in learning the longer term temporal context in the audio signals. CNNs and RNNs as classifiers have recently shown improved performances over established methods in various sound recognition tasks. We combine these two approaches in a convolutional recurrent neural network CRNN and apply it on a polyphonic sound event detection task. We compare the performance of the proposed CRNN method with CNN, RNN, and other established methods, and observe a considerable improvement for four different datasets consisting of everyday sound events.

Connected through hyperedge HE-1 (induced by source paper "sound event detection with depthwise separable and dilated convolutions"):
  with members:
    - "language modelling for sound event detection with teacher forcing and scheduled sampling"
    - "sound event localization and detection of overlapping sources using convolutional recurrent neural networks"
    - "network decoupling from regular to depthwise separable convolutions"
    - "listening for sirens locating and classifying acoustic alarms in city scenes"
    - "convolutional recurrent neural networks for bird audio detection"
    - "surrey cvssp system for dcase2017 challenge task 4"
    - "stacked convolutional and recurrent neural networks for music emotion recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-2 (induced by source paper "non negative matrix factorization convolutional neural network nmf cnn for sound event detection"):
  with members:
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "hodgepodge sound event detection based on ensemble of semi supervised learning methods"
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "a joint separation classification model for sound event detection of weakly labelled data"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "sound event detection in multichannel audio using spatial and harmonic features"
    - "pcanet a simple deep learning baseline for image classification"

Connected through hyperedge HE-3 (induced by source paper "duration robust weakly supervised sound event detection"):
  with members:
    - "cosine similarity penalty to discriminate sound classes in weakly supervised sound event detection"
    - "specialized decision surface and disentangled feature for weakly supervised polyphonic sound event detection"
    - "connectionist temporal localization for sound event detection with sequential labeling"
    - "large scale weakly labeled semi supervised sound event detection in domestic environments"
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "capsule routing for sound event detection"
    - "a light cnn for deep face representation with noisy labels"

Connected through hyperedge HE-4 (induced by source paper "cure dataset ladder networks for audio event classification"):
  with members:
    - "dcase 2018 challenge surrey cross task convolutional neural network baseline"
    - "dcase 2018 challenge baseline with convolutional neural networks"
    - "knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes"
    - "audio event detection using weakly labeled data"
    - "soundnet learning sound representations from unlabeled video"
    - "deconstructing the ladder network architecture"
    - "semi supervised learning with ladder networks"

Connected through hyperedge HE-5 (induced by source paper "sound event detection by multitask learning of sound events and scenes with soft scene labels"):
  with members:
    - "joint analysis of acoustic events and scenes based on multitask learning"
    - "surrey cvssp system for dcase2017 challenge task 4"
    - "cnn architectures for large scale audio classification"

Connected through hyperedge HE-6 (induced by source paper "deep progressive multi scale attention for acoustic event classification"):
  with members:
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "learning environmental sounds with multi scale convolutional neural network"
    - "attention based deep multiple instance learning"
    - "multiple instance deep learning for weakly supervised small footprint audio event detection"
    - "deep cnn framework for audio event recognition using weakly labeled web data"
    - "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "large scale weakly supervised audio classification using gated convolutional neural network"

Connected through hyperedge HE-7 (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"
    - "comparing the max and noisy or pooling functions in multiple instance learning for weakly supervised sequence learning tasks"
    - "adaptive pooling operators for weakly labeled sound event detection"

Connected through hyperedge HE-8 (induced by source paper "finding strength in weakness learning to separate sounds with weak supervision"):
  with members:
    - "phasebook and friends leveraging discrete representations for source separation"
    - "audio source separation using variational autoencoders and weak class supervision"
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "cutting music source separation some slakh a dataset to study the impact of training data quality and quantity"
    - "universal sound separation"
    - "co separating sounds of visual objects"
    - "sdr half baked or well done"

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.LG(Machine Learning)