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: "audeep unsupervised learning of representations from audio with deep recurrent neural networks"
  Abstract: auDeep is a Python toolkit for deep unsupervised representation learning from acoustic data. It is based on a recurrent sequence to sequence autoencoder approach which can learn representations of time series data by taking into account their temporal dynamics. We provide an extensive command line interface in addition to a Python API for users and developers, both of which are comprehensively documented and publicly available at this https URL Experimental results indicate that auDeep features are competitive with state-of-the art audio classification.

Connected through hyperedge HE-1 (induced by source paper "bottom up broadcast neural network for music genre classification"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "randomly weighted cnns for music audio classification"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "dlr toward a deep learned rhythmic representation for music content analysis"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "beyond low rank representations orthogonal clustering basis reconstruction with optimized graph structure for multi view spectral clustering"
    - "transfer learning for music classification and regression tasks"

Connected through hyperedge HE-2 (induced by source paper "n hans introducing the augsburg neuro holistic audio enhancement system"):
  with members:
    - "voxceleb2 deep speaker recognition"
    - "supervised speech separation based on deep learning an overview"
    - "voxceleb a large scale speaker identification dataset"
    - "openxbow introducing the passau open source crossmodal bag of words toolkit"
    - "deep clustering discriminative embeddings for segmentation and separation"

Connected through hyperedge HE-3 (induced by source paper "deep neural baselines for computational paralinguistics"):
  with members:
    - "deep learning for human affect recognition insights and new developments"
    - "learning transferable architectures for scalable image recognition"
    - "openxbow introducing the passau open source crossmodal bag of words toolkit"

Connected through hyperedge HE-4 (induced by source paper "recurrent neural network based semantic variational autoencoder for sequence to sequence learning"):
  with members:
    - "autoencoder with recurrent neural networks for video forgery detection"
    - "attention is all you need"
    - "unsupervised neural machine translation"
    - "machine comprehension by text to text neural question generation"
    - "variational autoencoders for semi supervised text classification"
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "squad 100 000 questions for machine comprehension of text"
    - "variational neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "an ensemble of transfer semi supervised and supervised learning methods for pathological heart sound classification"):
  with members:
    - "how transferable are features in deep neural networks"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

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)