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: "lcanet end to end lipreading with cascaded attention ctc"
  Abstract: Machine lipreading is a special type of automatic speech recognition (ASR) which transcribes human speech by visually interpreting the movement of related face regions including lips, face, and tongue. Recently, deep neural network based lipreading methods show great potential and have exceeded the accuracy of experienced human lipreaders in some benchmark datasets. However, lipreading is still far from being solved, and existing methods tend to have high error rates on the wild data. In this paper, we propose LCANet, an end-to-end deep neural network based lipreading system. LCANet encodes input video frames using a stacked 3D convolutional neural network (CNN), highway network and bidirectional GRU network. The encoder effectively captures both short-term and long-term spatio-temporal information. More importantly, LCANet incorporates a cascaded attention-CTC decoder to generate output texts. By cascading CTC with attention, it partially eliminates the defect of the conditional independence assumption of CTC within the hidden neural layers, and this yields notably performance improvement as well as faster convergence. The experimental results show the proposed system achieves a 1.3% CER and 3.0% WER on the GRID corpus database, leading to a 12.3% improvement compared to the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "can we read speech beyond the lips rethinking roi selection for deep visual speech recognition"):
  with members:
    - "a cascade sequence to sequence model for chinese mandarin lip reading"
    - "deep audio visual speech recognition"
    - "end to end audiovisual speech recognition"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "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"
    - "words can shift dynamically adjusting word representations using nonverbal behaviors"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-2 (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"
    - "lrs3 ted a large scale dataset for visual speech recognition"
    - "end to end audiovisual speech recognition"
    - "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-3 (induced by source paper "spatial group wise enhance improving semantic feature learning in convolutional networks"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "searching for mobilenetv3"
    - "selective kernel networks"
    - "weight standardization"
    - "bam bottleneck attention module"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "image super resolution using very deep residual channel attention networks"

Connected through hyperedge HE-4 (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"
    - "direct acoustics to word models for english conversational speech recognition"
    - "lipnet end to end sentence level lipreading"
    - "end to end visual speech recognition with lstms"
    - "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-5 (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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "zero shot keyword spotting for visual speech recognition in the wild"):
  with members:
    - "deep lip reading a comparison of models and an online application"
    - "espnet end to end speech processing toolkit"
    - "end to end audiovisual speech recognition"
    - "improving speaker independent lipreading with domain adversarial training"
    - "attention is all you need"
    - "deep word embeddings for visual speech recognition"
    - "direct acoustics to word models for english conversational speech recognition"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"

Connected through hyperedge HE-7 (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"
    - "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"

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)