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: "dnn driven speaker independent audio visual mask estimation for speech separation"
  Abstract: Human auditory cortex excels at selectively suppressing background noise to focus on a target speaker. The process of selective attention in the brain is known to contextually exploit the available audio and visual cues to better focus on target speaker while filtering out other noises. In this study, we propose a novel deep neural network (DNN) based audiovisual (AV) mask estimation model. The proposed AV mask estimation model contextually integrates the temporal dynamics of both audio and noise-immune visual features for improved mask estimation and speech separation. For optimal AV features extraction and ideal binary mask (IBM) estimation, a hybrid DNN architecture is exploited that leverages the complementary strengths of a stacked long short term memory (LSTM) and convolution LSTM network. The comparative simulation results in terms of speech quality and intelligibility demonstrate the significant performance improvement of our proposed AV mask estimation model as compared to audio-only and visual-only mask estimation approaches for both speaker dependent and independent scenarios.

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 "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"
    - "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"
    - "segan speech enhancement generative adversarial network"

Connected through hyperedge HE-3 (induced by source paper "prediction of physical load level by machine learning analysis of heart activity after exercises"):
  with members:
    - "parallel statistical and machine learning methods for estimation of physical load"
    - "user driven intelligent interface on the basis of multimodal augmented reality and brain computer interaction for people with functional disabilities"
    - "augmented coaching ecosystem for non obtrusive adaptive personalized elderly care on the basis of cloud fog dew computing paradigm"
    - "synergy of volunteer measurements and volunteer computing for effective data collecting processing simulating and analyzing on a worldwide scale"

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"
    - "audio visual speech enhancement based on multimodal deep convolutional neural network"

Connected through hyperedge HE-5 (induced by source paper "contextual audio visual switching for speech enhancement in real world environments"):
  with members:
    - "audio visual speech enhancement using multimodal deep convolutional neural networks"
    - "lip reading sentences in the wild"
    - "multi modal hybrid deep neural network for speech enhancement"

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)