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 time and frequency domain for audio event recognition using deep learning"
  Abstract: Recognizing acoustic events is an intricate problem for a machine and an emerging field of research. Deep neural networks achieve convincing results and are currently the state-of-the-art approach for many tasks. One advantage is their implicit feature learning, opposite to an explicit feature extraction of the input signal. In this work, we analyzed whether more discriminative features can be learned from either the time-domain or the frequency-domain representation of the audio signal. For this purpose, we trained multiple deep networks with different architectures on the Freiburg-106 and ESC-10 datasets. Our results show that feature learning from the frequency domain is superior to the time domain. Moreover, additionally using convolution and pooling layers, to explore local structures of the audio signal, significantly improves the recognition performance and achieves state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "nnaudio an on the fly gpu audio to spectrogram conversion toolbox using 1d convolution neural networks"):
  with members:
    - "adversarial learning for improved onsets and frames music transcription"
    - "towards interpretable polyphonic transcription with invertible neural networks"
    - "an attention mechanism for musical instrument recognition"
    - "zero shot learning for audio based music classification and tagging"
    - "learning soft attention models for tempo invariant audio sheet music retrieval"
    - "a bi directional transformer for musical chord recognition"
    - "cover detection using dominant melody embeddings"
    - "kapre on gpu audio preprocessing layers for a quick implementation of deep neural network models with keras"

Connected through hyperedge HE-2 (induced by source paper "joining sound event detection and localization through spatial segregation"):
  with members:
    - "the nigens general sound events database"
    - "sound event localization and detection of overlapping sources using convolutional recurrent neural networks"
    - "robust audio event recognition with 1 max pooling convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "fourier rnns for sequence prediction"):
  with members:
    - "neural decomposition of time series data for effective generalization"
    - "complex gated recurrent neural networks"
    - "better than real complex valued neural nets for mri fingerprinting"
    - "gated orthogonal recurrent units on learning to forget"
    - "deep complex networks"
    - "multiresolution recurrent neural networks an application to dialogue response generation"
    - "hierarchical multiscale recurrent neural networks"
    - "unitary evolution recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "fourier rnns for sequence analysis and prediction"):
  with members:
    - "neural decomposition of time series data for effective generalization"
    - "complex gated recurrent neural networks"
    - "gated orthogonal recurrent units on learning to forget"
    - "deep complex networks"
    - "unitary evolution recurrent neural networks"
    - "a clockwork rnn"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "fast training of convolutional networks through ffts"

Connected through hyperedge HE-5 (induced by source paper "end to end polyphonic sound event detection using convolutional recurrent neural networks with learned time frequency representation input"):
  with members:
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "sound event detection in multichannel audio using spatial and harmonic features"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-6 (induced by source paper "soundnet learning sound representations from unlabeled video"):
  with members:
    - "learning aligned cross modal representations from weakly aligned data"
    - "generating videos with scene dynamics"
    - "yfcc100m the new data in multimedia research"
    - "acoustic scene classification classifying environments from the sounds they produce"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "anticipating visual representations from unlabeled video"
    - "cross modal distillation for supervision transfer"
    - "visually indicated sounds"

Connected through hyperedge HE-7 (induced by source paper "classifying variable length audio files with all convolutional networks and masked global pooling"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "robust audio event recognition with 1 max pooling convolutional neural networks"):
  with members:
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"

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.NE(Neural and Evolutionary Computing)