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: "lipreading with long short term memory"
  Abstract: Lipreading, i.e. speech recognition from visual-only recordings of a speaker's face, can be achieved with a processing pipeline based solely on neural networks, yielding significantly better accuracy than conventional methods. Feed-forward and recurrent neural network layers (namely Long Short-Term Memory; LSTM) are stacked to form a single structure which is trained by back-propagating error gradients through all the layers. The performance of such a stacked network was experimentally evaluated and compared to a standard Support Vector Machine classifier using conventional computer vision features (Eigenlips and Histograms of Oriented Gradients). The evaluation was performed on data from 19 speakers of the publicly available GRID corpus. With 51 different words to classify, we report a best word accuracy on held-out evaluation speakers of 79.6% using the end-to-end neural network-based solution (11.6% improvement over the best feature-based solution evaluated).

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"
    - "end to end multi view lipreading"
    - "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 "deep audio visual speech recognition"):
  with members:
    - "deep lip reading a comparison of models and an online application"
    - "large scale visual speech recognition"
    - "lrs3 ted a large scale dataset for 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"

Connected through hyperedge HE-5 (induced by source paper "learning spatio temporal features with two stream deep 3d cnns for lipreading"):
  with members:
    - "deep audio visual speech recognition"
    - "future near collision prediction from monocular video feasibility dataset and challenges"
    - "end to end audiovisual speech recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning spatio temporal features with 3d residual networks for action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"

Connected through hyperedge HE-6 (induced by source paper "on the importance of video action recognition for visual lipreading"):
  with members:
    - "deep audio visual speech recognition"
    - "end to end audiovisual speech recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning spatio temporal features with 3d residual networks for action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"

Connected through hyperedge HE-7 (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-8 (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"
    - "end to end multi view lipreading"
    - "combining residual networks with lstms for lipreading"
    - "lip reading sentences in the wild"

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)