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: "audio scene classification with deep recurrent neural networks"
  Abstract: We introduce in this work an efficient approach for audio scene classification using deep recurrent neural networks. A scene audio signal is firstly transformed into a sequence of high-level label tree embedding feature vectors. The vector sequence is then divided into multiple subsequences on which a deep GRU-based recurrent neural network is trained for sequence-to-label classification. The global predicted label for the entire sequence is finally obtained via aggregation of subsequence classification outputs. We will show that our approach obtain an F1-score of 97.7% on the LITIS Rouen dataset, which is the largest dataset publicly available for the task. Compared to the best previously reported result on the dataset, our approach is able to reduce the relative classification error by 35.3%.

Connected through hyperedge HE-1 (induced by source paper "an open set recognition and few shot learning dataset for audio event classification in domestic environments"):
  with members:
    - "deep face recognition a survey"
    - "training neural audio classifiers with few data"
    - "ring loss convex feature normalization for face recognition"
    - "recent advances in open set recognition a survey"
    - "look listen and learn"
    - "icnet for real time semantic segmentation on high resolution images"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "tristounet triplet loss for speaker turn embedding"

Connected through hyperedge HE-2 (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-3 (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"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "label tree embeddings for acoustic scene classification"

Connected through hyperedge HE-4 (induced by source paper "seqsleepnet end to end hierarchical recurrent neural network for sequence to sequence automatic sleep staging"):
  with members:
    - "joint classification and prediction cnn framework for automatic sleep stage classification"
    - "detection of rem sleep behaviour disorder by automated polysomnography analysis"
    - "neural network an1alysis of sleep stages enables efficient diagnosis of narcolepsy"
    - "end to end learning for self driving cars"
    - "recurrent batch normalization"
    - "effective approaches to attention based neural machine translation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-5 (induced by source paper "joint classification and prediction cnn framework for automatic sleep stage classification"):
  with members:
    - "neural network an1alysis of sleep stages enables efficient diagnosis of narcolepsy"
    - "robust audio event recognition with 1 max pooling convolutional neural networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-6 (induced by source paper "beyond equal length snippets how long is sufficient to recognize an audio scene"):
  with members:
    - "label tree embeddings for acoustic scene classification"
    - "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-7 (induced by source paper "acoustic features fusion using attentive multi channel deep architecture"):
  with members:
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "unsupervised feature learning based on deep models for environmental audio tagging"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "convolutional gated recurrent neural network incorporating spatial features for audio tagging"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"

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)