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: "lrs3 ted a large scale dataset for visual speech recognition"
  Abstract: This paper introduces a new multi-modal dataset for visual and audio-visual speech recognition. It includes face tracks from over 400 hours of TED and TEDx videos, along with the corresponding subtitles and word alignment boundaries. The new dataset is substantially larger in scale compared to other public datasets that are available for general research.

Connected through hyperedge HE-1 (induced by source paper "mutual information maximization for effective lip reading"):
  with members:
    - "multi grained spatio temporal modeling for lip reading"
    - "information maximizing visual question generation"
    - "high resolution talking face generation via mutual information approximation"
    - "end to end audiovisual speech recognition"
    - "lcanet end to end lipreading with cascaded attention ctc"
    - "lrw 1000 a naturally distributed large scale benchmark for lip reading in the wild"
    - "pushing the boundaries of audiovisual word recognition using residual networks and lstms"
    - "improving speaker independent lipreading with domain adversarial training"

Connected through hyperedge HE-2 (induced by source paper "deep audio visual speech recognition"):
  with members:
    - "deep lip reading a comparison of models and an online application"
    - "large scale visual speech recognition"
    - "end to end audiovisual speech recognition"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "attention is all you need"
    - "wav2letter an end to end convnet based speech recognition system"
    - "lipnet end to end sentence level lipreading"
    - "residual convolutional ctc networks for automatic speech recognition"

Connected through hyperedge HE-3 (induced by source paper "learning from videos with deep convolutional lstm networks"):
  with members:
    - "deep audio visual speech recognition"
    - "deep lip reading a comparison of models and an online application"
    - "are all languages equally hard to language model"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "revisiting the effectiveness of off the shelf temporal modeling approaches for large scale video classification"
    - "attention is all you need"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "understanding the effective receptive field in deep convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "animating face using disentangled audio representations"):
  with members:
    - "capture learning and synthesis of 3d speaking styles"
    - "the conversation deep audio visual speech enhancement"
    - "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"
    - "visemenet audio driven animator centric speech animation"
    - "speech driven facial reenactment using conditional generative adversarial networks"
    - "everybody dance now"

Connected through hyperedge HE-5 (induced by source paper "asr is all you need cross modal distillation for lip reading"):
  with members:
    - "deep audio visual speech recognition"
    - "guiding ctc posterior spike timings for improved posterior fusion and knowledge distillation"
    - "deep lip reading a comparison of models and an online application"
    - "large scale visual speech recognition"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "emotion recognition in speech using cross modal transfer in the wild"
    - "voxceleb2 deep speaker recognition"
    - "audio visual speech recognition with a hybrid ctc attention architecture"

Connected through hyperedge HE-6 (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"
    - "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"
    - "audio visual speech enhancement using multimodal deep convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "large scale visual speech recognition"):
  with members:
    - "deep audio visual speech recognition"
    - "the conversation deep audio visual speech enhancement"
    - "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"
    - "vid2speech speech reconstruction from silent video"

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)