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 visual speech recognition with lstms"
  Abstract: Traditional visual speech recognition systems consist of two stages, feature extraction and classification. Recently, several deep learning approaches have been presented which automatically extract features from the mouth images and aim to replace the feature extraction stage. However, research on joint learning of features and classification is very limited. In this work, we present an end-to-end visual speech recognition system based on Long-Short Memory (LSTM) networks. To the best of our knowledge, this is the first model which simultaneously learns to extract features directly from the pixels and perform classification and also achieves state-of-the-art performance in visual speech classification. The model consists of two streams which extract features directly from the mouth and difference images, respectively. The temporal dynamics in each stream are modelled by an LSTM and the fusion of the two streams takes place via a Bidirectional LSTM (BLSTM). An absolute improvement of 9.7% over the base line is reported on the OuluVS2 database, and 1.5% on the CUAVE database when compared with other methods which use a similar visual front-end.

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 "lipreading with 3d 2d cnn blstm hmm and word ctc models"):
  with members:
    - "deep audio visual speech recognition"
    - "large scale visual speech recognition"
    - "lcanet end to end lipreading with cascaded attention ctc"
    - "direct acoustics to word models for english conversational speech recognition"
    - "lipnet end to end sentence level lipreading"
    - "multi task learning of deep neural networks for audio visual automatic speech recognition"
    - "yolo9000 better faster stronger"
    - "lip reading sentences in the wild"

Connected through hyperedge HE-3 (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 multi view lipreading"
    - "combining residual networks with lstms for lipreading"
    - "lipreading with long short term memory"
    - "lip reading sentences in the wild"

Connected through hyperedge HE-4 (induced by source paper "towards pose invariant lip reading"):
  with members:
    - "deep audio visual speech recognition"
    - "face alignment in full pose range a 3d total solution"
    - "end to end audiovisual speech recognition"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "lipnet end to end sentence level lipreading"
    - "lipreading with long short term memory"
    - "do we really need to collect millions of faces for effective face recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "lcanet end to end lipreading with cascaded attention ctc"):
  with members:
    - "progressive joint modeling in unsupervised single channel overlapped speech recognition"
    - "draps dynamic and resource aware placement scheme for docker containers in a heterogeneous cluster"
    - "deeprebirth accelerating deep neural network execution on mobile devices"
    - "attention is all you need"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "reconstruction for feature disentanglement in pose invariant face recognition"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-6 (induced by source paper "audio visual speech recognition with a hybrid ctc attention architecture"):
  with members:
    - "deep audio visual speech recognition"
    - "large scale visual speech recognition"
    - "espnet end to end speech processing toolkit"
    - "end to end audiovisual speech recognition"
    - "lcanet end to end lipreading with cascaded attention ctc"
    - "lipnet end to end sentence level lipreading"
    - "combining residual networks with lstms for lipreading"
    - "lipreading with long short term memory"

Connected through hyperedge HE-7 (induced by source paper "end to end audiovisual speech recognition"):
  with members:
    - "end to end multimodal emotion recognition using deep neural networks"
    - "lipnet end to end sentence level lipreading"
    - "combining residual networks with lstms for lipreading"
    - "lipreading with long short term memory"
    - "lip reading sentences in the wild"
    - "deep multimodal learning for audio visual speech recognition"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "end to end audiovisual speech activity detection with bimodal recurrent neural models"):
  with members:
    - "very deep convolutional networks for end to end speech recognition"
    - "lip reading sentences in the wild"
    - "very deep multilingual convolutional neural networks for lvcsr"
    - "end to end attention based large vocabulary speech recognition"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "adam a method for stochastic optimization"
    - "deep speech scaling up end to end speech recognition"
    - "speech recognition with deep recurrent neural networks"

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)