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 visual speech enhancement using multimodal deep convolutional neural networks"
  Abstract: Speech enhancement (SE) aims to reduce noise in speech signals. Most SE techniques focus only on addressing audio information. In this work, inspired by multimodal learning, which utilizes data from different modalities, and the recent success of convolutional neural networks (CNNs) in SE, we propose an audio-visual deep CNNs (AVDCNN) SE model, which incorporates audio and visual streams into a unified network model. We also propose a multi-task learning framework for reconstructing audio and visual signals at the output layer. Precisely speaking, the proposed AVDCNN model is structured as an audio-visual encoder-decoder network, in which audio and visual data are first processed using individual CNNs, and then fused into a joint network to generate enhanced speech (the primary task) and reconstructed images (the secondary task) at the output layer. The model is trained in an end-to-end manner, and parameters are jointly learned through back-propagation. We evaluate enhanced speech using five instrumental criteria. Results show that the AVDCNN model yields a notably superior performance compared with an audio-only CNN-based SE model and two conventional SE approaches, confirming the effectiveness of integrating visual information into the SE process. In addition, the AVDCNN model also outperforms an existing audio-visual SE model, confirming its capability of effectively combining audio and visual information in SE.

Connected through hyperedge HE-1 (induced by source paper "audio visual speech enhancement using conditional variational auto encoder"):
  with members:
    - "semi supervised multichannel speech enhancement with variational autoencoders and non negative matrix factorization"
    - "a statistically principled and computationally efficient approach to speech enhancement using variational autoencoders"
    - "a deep generative model of speech complex spectrograms"
    - "speech enhancement with variational autoencoders and alpha stable distributions"
    - "statistical speech enhancement based on probabilistic integration of variational autoencoder and non negative matrix factorization"
    - "the conversation deep audio visual speech enhancement"
    - "end to end audiovisual speech recognition"
    - "fast mvae joint separation and classification of mixed sources based on multichannel variational autoencoder with auxiliary classifier"

Connected through hyperedge HE-2 (induced by source paper "mmtm multimodal transfer module for cnn fusion"):
  with members:
    - "my lips are concealed audio visual speech enhancement through obstructions"
    - "mfas multimodal fusion architecture search"
    - "the conversation deep audio visual speech enhancement"
    - "centralnet a multilayer approach for multimodal fusion"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "improving the performance of unimodal dynamic hand gesture recognition with multimodal training"

Connected through hyperedge HE-3 (induced by source paper "lisht non parametric linearly scaled hyperbolic tangent activation function for neural networks"):
  with members:
    - "average biased relu based cnn descriptor for improved face retrieval"
    - "complex valued neural networks with non parametric activation functions"
    - "dual cnn models for unsupervised monocular depth estimation"
    - "a performance evaluation of convolutional neural networks for face anti spoofing"
    - "rccnet an efficient convolutional neural network for histological routine colon cancer nuclei classification"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "dfunet convolutional neural networks for diabetic foot ulcer classification"
    - "visualizing the loss landscape of neural nets"

Connected through hyperedge HE-4 (induced by source paper "cochleanet a robust language independent audio visual model for speech enhancement"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "sdr half baked or well done"
    - "dnn driven speaker independent audio visual mask estimation for speech separation"
    - "the sound of pixels"
    - "lip reading driven deep learning approach for speech enhancement"
    - "audio visual scene analysis with self supervised multisensory features"
    - "supervised speech separation based on deep learning an overview"
    - "language and noise transfer in speech enhancement generative adversarial network"

Connected through hyperedge HE-5 (induced by source paper "my lips are concealed audio visual speech enhancement through obstructions"):
  with members:
    - "deep audio visual speech recognition"
    - "the conversation deep audio visual speech enhancement"
    - "lrs3 ted a large scale dataset for visual speech recognition"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "voxceleb2 deep speaker recognition"
    - "supervised speech separation based on deep learning an overview"
    - "visual speech enhancement"
    - "seeing through noise visually driven speaker separation and enhancement"

Connected through hyperedge HE-6 (induced by source paper "artificial neural networks in action for an automated cell type classification of biological neural networks"):
  with members:
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "empirical evaluation of rectified activations in convolutional network"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"):
  with members:
    - "the conversation deep audio visual speech enhancement"
    - "learning to separate object sounds by watching unlabeled video"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "the sound of pixels"
    - "audio visual scene analysis with self supervised multisensory features"
    - "improved speech reconstruction from silent video"
    - "supervised speech separation based on deep learning an overview"
    - "visual speech enhancement"

Connected through hyperedge HE-8 (induced by source paper "the conversation deep audio visual speech enhancement"):
  with members:
    - "end to end audiovisual speech recognition"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "improved speech reconstruction from silent video"
    - "supervised speech separation based on deep learning an overview"
    - "visual speech enhancement"
    - "seeing through noise visually driven speaker separation and enhancement"
    - "does phase matter for monaural source separation"
    - "lipnet end to end sentence level lipreading"

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)