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: "end to end multi view lipreading"
  Abstract: Non-frontal lip views contain useful information which can be used to enhance the performance of frontal view lipreading. However, the vast majority of recent lipreading works, including the deep learning approaches which significantly outperform traditional approaches, have focused on frontal mouth images. As a consequence, research on joint learning of visual features and speech classification from multiple views is limited. In this work, we present an end-to-end multi-view lipreading system based on Bidirectional Long-Short Memory (BLSTM) networks. To the best of our knowledge, this is the first model which simultaneously learns to extract features directly from the pixels and performs visual speech classification from multiple views and also achieves state-of-the-art performance. The model consists of multiple identical streams, one for each view, which extract features directly from different poses of mouth images. The temporal dynamics in each stream/view are modelled by a BLSTM and the fusion of multiple streams/views takes place via another BLSTM. An absolute average improvement of 3% and 3.8% over the frontal view performance is reported on the OuluVS2 database when the best two (frontal and profile) and three views (frontal, profile, 45) are combined, respectively. The best three-view model results in a 10.5% absolute improvement over the current multi-view state-of-the-art performance on OuluVS2, without using external databases for training, achieving a maximum classification accuracy of 96.9%.

Connected through hyperedge HE-1 (induced by source paper "lipreading using temporal convolutional networks"):
  with members:
    - "deep audio visual speech recognition"
    - "learning spatio temporal features with two stream deep 3d cnns for lipreading"
    - "multi grained spatio temporal modeling for lip reading"
    - "large scale visual speech recognition"
    - "end to end audiovisual speech recognition"
    - "lrw 1000 a naturally distributed large scale benchmark for lip reading in the wild"
    - "audio visual speech recognition with a hybrid ctc attention architecture"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"

Connected through hyperedge HE-2 (induced by source paper "harnessing gans for zero shot learning of new classes in visual speech recognition"):
  with members:
    - "modality attention for end to end audio visual speech recognition"
    - "low shot learning from imaginary data"
    - "harnessing ai for speech reconstruction using multi view silent video feed"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "lipreading with long short term memory"
    - "matching networks for one shot learning"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (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-4 (induced by source paper "harnessing gans for addition of new classes in vsr"):
  with members:
    - "generative adversarial networks an overview"
    - "modality attention for end to end audio visual speech recognition"
    - "low shot learning from imaginary data"
    - "cold start aware user and product attention for sentiment classification"
    - "harnessing ai for speech reconstruction using multi view silent video feed"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "lip2audspec speech reconstruction from silent lip movements video"
    - "a generative adversarial approach for zero shot learning from noisy texts"

Connected through hyperedge HE-5 (induced by source paper "end to end visual speech recognition for small scale datasets"):
  with members:
    - "large scale visual speech recognition"
    - "end to end audiovisual speech recognition"
    - "end to end audiovisual fusion with lstms"
    - "lipnet end to end sentence level lipreading"
    - "end to end visual speech recognition with lstms"
    - "combining residual networks with lstms for lipreading"
    - "lipreading with long short term memory"
    - "lip reading sentences in the wild"

Connected through hyperedge HE-6 (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-7 (induced by source paper "harnessing ai for speech reconstruction using multi view silent video feed"):
  with members:
    - "improved speech reconstruction from silent video"
    - "lip2audspec speech reconstruction from silent lip movements video"
    - "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)