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: "label tree embeddings for acoustic scene classification"
  Abstract: We present in this paper an efficient approach for acoustic scene classification by exploring the structure of class labels. Given a set of class labels, a category taxonomy is automatically learned by collectively optimizing a clustering of the labels into multiple meta-classes in a tree structure. An acoustic scene instance is then embedded into a low-dimensional feature representation which consists of the likelihoods that it belongs to the meta-classes. We demonstrate state-of-the-art results on two different datasets for the acoustic scene classification task, including the DCASE 2013 and LITIS Rouen datasets.

Connected through hyperedge HE-1 (induced by source paper "robust acoustic scene classification using a multi spectrogram encoder decoder framework"):
  with members:
    - "enhancing sound texture in cnn based acoustic scene classification"
    - "acoustic scene classification by implicitly identifying distinct sound events"
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "spatio temporal attention pooling for audio scene classification"
    - "enabling early audio event detection with neural networks"
    - "a compact and discriminative feature based on auditory summary statistics for acoustic scene classification"
    - "dcase 2018 challenge surrey cross task convolutional neural network baseline"
    - "beyond equal length snippets how long is sufficient to recognize an audio scene"

Connected through hyperedge HE-2 (induced by source paper "spatio temporal attention pooling for audio scene classification"):
  with members:
    - "fine grained video categorization with redundancy reduction attention"
    - "beyond equal length snippets how long is sufficient to recognize an audio scene"
    - "mixup based acoustic scene classification using multi channel convolutional neural network"
    - "unifying isolated and overlapping audio event detection with multi label multi task convolutional recurrent neural networks"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "learning from between class examples for deep sound recognition"
    - "audio scene classification with deep recurrent neural networks"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"

Connected through hyperedge HE-3 (induced by source paper "mixup based acoustic scene classification using multi channel convolutional neural network"):
  with members:
    - "mixup beyond empirical risk minimization"
    - "convolutional gated recurrent neural network incorporating spatial features for audio tagging"
    - "sound event detection in multichannel audio using spatial and harmonic features"
    - "classifying variable length audio files with all convolutional networks and masked global pooling"
    - "xception deep learning with depthwise separable convolutions"
    - "acoustic scene classification using convolutional neural network and multiple width frequency delta data augmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "beyond equal length snippets how long is sufficient to recognize an audio scene"):
  with members:
    - "audio scene classification with deep recurrent neural networks"
    - "robust audio event recognition with 1 max pooling convolutional neural networks"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "subspectralnet using sub spectrogram based convolutional neural networks for acoustic scene classification"):
  with members:
    - "squeeze and excitation networks"
    - "densely connected convolutional networks"
    - "the bag of frames approach a not so sufficient model for urban soundscapes"

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.MM(Multimedia)