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: "learning audio sequence representations for acoustic event classification"
  Abstract: Acoustic Event Classification (AEC) has become a significant task for machines to perceive the surrounding auditory scene. However, extracting effective representations that capture the underlying characteristics of the acoustic events is still challenging. Previous methods mainly focused on designing the audio features in a 'hand-crafted' manner. Interestingly, data-learnt features have been recently reported to show better performance. Up to now, these were only considered on the frame-level. In this paper, we propose an unsupervised learning framework to learn a vector representation of an audio sequence for AEC. This framework consists of a Recurrent Neural Network (RNN) encoder and a RNN decoder, which respectively transforms the variable-length audio sequence into a fixed-length vector and reconstructs the input sequence on the generated vector. After training the encoder-decoder, we feed the audio sequences to the encoder and then take the learnt vectors as the audio sequence representations. Compared with previous methods, the proposed method can not only deal with the problem of arbitrary-lengths of audio streams, but also learn the salient information of the sequence. Extensive evaluation on a large-size acoustic event database is performed, and the empirical results demonstrate that the learnt audio sequence representation yields a significant performance improvement by a large margin compared with other state-of-the-art hand-crafted sequence features for AEC.

Connected through hyperedge HE-1 (induced by source paper "snore gans improving automatic snore sound classification with synthesized data"):
  with members:
    - "generative adversarial networks an overview"
    - "adversarial training in affective computing and sentiment analysis recent advances and perspectives"
    - "data augmentation in emotion classification using generative adversarial networks"
    - "learning to fuse music genres with generative adversarial dual learning"
    - "segan speech enhancement generative adversarial network"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"

Connected through hyperedge HE-2 (induced by source paper "attention augmented end to end multi task learning for emotion prediction from speech"):
  with members:
    - "dynamic difficulty awareness training for continuous emotion prediction"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "tacotron towards end to end speech synthesis"
    - "an overview of multi task learning in deep neural networks"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "end to end text dependent speaker verification"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "recurrent models of visual attention"

Connected through hyperedge HE-3 (induced by source paper "acoustic scene classification using multi layer temporal pooling based on convolutional neural network"):
  with members:
    - "a compact and discriminative feature based on auditory summary statistics for acoustic scene classification"
    - "rank pooling for action recognition"
    - "deep convolutional neural networks and data augmentation for acoustic event detection"
    - "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 "dcase 2017 task 1 acoustic scene classification using shift invariant kernels and random features"):
  with members:
    - "look listen and learn"
    - "experiments on the dcase challenge 2016 acoustic scene classification and sound event detection in real life recording"
    - "soundnet learning sound representations from unlabeled video"

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)