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: "vid2speech speech reconstruction from silent video"
  Abstract: Speechreading is a notoriously difficult task for humans to perform. In this paper we present an end-to-end model based on a convolutional neural network (CNN) for generating an intelligible acoustic speech signal from silent video frames of a speaking person. The proposed CNN generates sound features for each frame based on its neighboring frames. Waveforms are then synthesized from the learned speech features to produce intelligible speech. We show that by leveraging the automatic feature learning capabilities of a CNN, we can obtain state-of-the-art word intelligibility on the GRID dataset, and show promising results for learning out-of-vocabulary (OOV) words.

Connected through hyperedge HE-1 (induced by source paper "vision infused deep audio inpainting"):
  with members:
    - "compound domain adaptation in an open world"
    - "recursive visual sound separation using minus plus net"
    - "the sound of motions"
    - "learning to localize sound source in visual scenes"
    - "lip movements generation at a glance"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "self supervised generation of spatial audio for 360 video"

Connected through hyperedge HE-2 (induced by source paper "large scale visual speech recognition"):
  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"
    - "lcanet end to end lipreading with cascaded attention ctc"
    - "exploring architectures data and units for streaming end to end speech recognition with rnn transducer"
    - "improving speaker independent lipreading with domain adversarial training"
    - "lip2audspec speech reconstruction from silent lip movements video"

Connected through hyperedge HE-3 (induced by source paper "dynamic temporal alignment of speech to lips"):
  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"
    - "egosampling wide view hyperlapse from egocentric videos"
    - "audio visual scene analysis with self supervised multisensory features"
    - "improved speech reconstruction from silent video"
    - "you said that"
    - "lip reading sentences in the wild"
    - "deep multimodal learning for audio visual speech recognition"

Connected through hyperedge HE-4 (induced by source paper "improved speech reconstruction from silent video"):
  with members:
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "seeing through noise visually driven speaker separation and enhancement"
    - "tacotron towards end to end speech synthesis"
    - "lipnet end to end sentence level lipreading"
    - "end to end visual speech recognition with lstms"
    - "decoding visemes improving machine lipreading"
    - "combining residual networks with lstms for lipreading"
    - "lipreading with long short term memory"

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"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "segan speech enhancement generative adversarial network"
    - "seeing through noise visually driven speaker separation and enhancement"
    - "lipnet end to end sentence level lipreading"

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"
    - "lipnet end to end sentence level lipreading"
    - "audio visual speech enhancement based on multimodal deep convolutional neural network"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "lip2audspec speech reconstruction from silent lip movements video"):
  with members:
    - "lipnet end to end sentence level lipreading"
    - "lipreading with long short term memory"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "visually indicated sounds"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "updating the silent speech challenge benchmark with deep learning"):
  with members:
    - "development of a 3d tongue motion visualization platform based on ultrasound image sequences"
    - "tongue contour extraction from ultrasound images based on deep neural network"

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.CV(Computer Vision and Pattern Recognition)