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: "improved speech reconstruction from silent video"
  Abstract: Speechreading is the task of inferring phonetic information from visually observed articulatory facial movements, and 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 and natural-sounding acoustic speech signal from silent video frames of a speaking person. We train our model on speakers from the GRID and TCD-TIMIT datasets, and evaluate the quality and intelligibility of reconstructed speech using common objective measurements. We show that speech predictions from the proposed model attain scores which indicate significantly improved quality over existing models. In addition, we show promising results towards reconstructing speech from an unconstrained dictionary.

Connected through hyperedge HE-1 (induced by source paper "mobivsr a visual speech recognition solution for mobile devices"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "model compression via distillation and quantization"
    - "learning motion in feature space locally consistent deformable convolution networks for fine grained action detection"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "harnessing ai for speech reconstruction using multi view silent video feed"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "dynamically hierarchy revolution dirnet for compressing recurrent neural network on mobile devices"
    - "3d depthwise convolution reducing model parameters in 3d vision tasks"

Connected through hyperedge HE-2 (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"
    - "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"
    - "lipnet end to end sentence level lipreading"

Connected through hyperedge HE-3 (induced by source paper "wav2pix speech conditioned face generation using generative adversarial networks"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "began boundary equilibrium generative adversarial networks"
    - "segan speech enhancement generative adversarial network"
    - "voxceleb a large scale speaker identification dataset"
    - "deep cross modal audio visual generation"
    - "you said that"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-4 (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"
    - "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-5 (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"
    - "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"
    - "audio visual speech enhancement using multimodal deep convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "face landmark based speaker independent audio visual speech enhancement in multi talker environments"):
  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"
    - "deep attractor network for single microphone speaker separation"
    - "visual speech enhancement"
    - "seeing through noise visually driven speaker separation and enhancement"
    - "audio visual speech enhancement using multimodal deep convolutional neural networks"
    - "single channel multi speaker separation using deep clustering"

Connected through hyperedge HE-7 (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"
    - "vid2speech 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-8 (induced by source paper "harnessing ai for speech reconstruction using multi view silent video feed"):
  with members:
    - "lip2audspec speech reconstruction from silent lip movements video"
    - "end to end multi view lipreading"
    - "lipreading with long short term memory"
    - "visual speech recognition using pca networks and lstms in a tandem gmm hmm system"
    - "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.CV(Computer Vision and Pattern Recognition)