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 based on multimodal deep convolutional neural network"
  Abstract: Speech enhancement (SE) aims to reduce noise in speech signals. Most SE techniques focus on addressing audio information only. 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 CNN (AVDCNN) SE model, which incorporates audio and visual streams into a unified network model. In the proposed AVDCNN SE model, audio and visual data are first processed using individual CNNs, and then, fused into a joint network to generate enhanced speech at the output layer. The AVDCNN model is trained in an end-to-end manner, and parameters are jointly learned through back-propagation. We evaluate enhanced speech using five objective criteria. Results show that the AVDCNN yields notably better 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.

Connected through hyperedge HE-1 (induced by source paper "latent variable algorithms for multimodal learning and sensor fusion"):
  with members:
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "learning to separate object sounds by watching unlabeled video"
    - "the sound of pixels"
    - "improved speech reconstruction from silent video"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "attention is all you need"
    - "visual speech enhancement"
    - "seeing through noise visually driven speaker separation and enhancement"

Connected through hyperedge HE-2 (induced by source paper "the history began from alexnet a comprehensive survey on deep learning approaches"):
  with members:
    - "hyperspectral image classification with markov random fields and a convolutional neural network"
    - "an analysis of deep neural network models for practical applications"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "a deep convolutional neural network for background subtraction"
    - "a survey of neuromorphic computing and neural networks in hardware"

Connected through hyperedge HE-3 (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-4 (induced by source paper "role of awareness and universal context in a spiking conscious neural network scnn a new perspective and future directions"):
  with members:
    - "contrasting information theoretic decompositions of modulatory and arithmetic interactions in neural information processing systems"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "contextual audio visual switching for speech enhancement in real world environments"
    - "dnn driven speaker independent audio visual mask estimation for speech separation"

Connected through hyperedge HE-5 (induced by source paper "visual speech enhancement"):
  with members:
    - "the conversation deep audio visual speech enhancement"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "audio visual scene analysis with self supervised multisensory features"
    - "improved speech reconstruction from silent video"
    - "vid2speech speech reconstruction from silent video"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "segan speech enhancement generative adversarial network"
    - "seeing through noise visually driven speaker separation and enhancement"

Connected through hyperedge HE-6 (induced by source paper "seeing through noise visually driven speaker separation and enhancement"):
  with members:
    - "improved speech reconstruction from silent video"
    - "vid2speech speech reconstruction from silent video"
    - "lipnet end to end sentence level lipreading"
    - "single channel multi speaker separation using deep clustering"
    - "lip reading sentences in the wild"
    - "soundnet learning sound representations from unlabeled video"
    - "deep residual learning for image recognition"
    - "visually indicated sounds"

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)