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: "decoding visemes improving machine lipreading"
  Abstract: To undertake machine lip-reading, we try to recognise speech from a visual signal. Current work often uses viseme classification supported by language models with varying degrees of success. A few recent works suggest phoneme classification, in the right circumstances, can outperform viseme classification. In this work we present a novel two-pass method of training phoneme classifiers which uses previously trained visemes in the first pass. With our new training algorithm, we show classification performance which significantly improves on previous lip-reading results.

Connected through hyperedge HE-1 (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-2 (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"
    - "lcanet end to end lipreading with cascaded attention ctc"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "the speaker independent lipreading play off a survey of lipreading machines"):
  with members:
    - "large scale visual speech recognition"
    - "audio visual speech recognition with a hybrid ctc attention architecture"
    - "comparing phonemes and visemes with dnn based lipreading"
    - "visual speech recognition aligning terminologies for better understanding"
    - "improving speaker independent lipreading with domain adversarial training"
    - "lipnet end to end sentence level lipreading"
    - "combining residual networks with lstms for lipreading"
    - "resolution limits on visual speech recognition"

Connected through hyperedge HE-4 (induced by source paper "resource aware design of a deep convolutional recurrent neural network for speech recognition through audio visual sensor fusion"):
  with members:
    - "lipnet end to end sentence level lipreading"
    - "lipreading with long short term memory"
    - "lip reading sentences in the wild"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "improved speech reconstruction from silent video"):
  with members:
    - "vid2speech speech reconstruction from silent video"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "seeing through noise visually driven speaker separation and enhancement"
    - "tacotron towards end to end speech synthesis"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "combining residual networks with lstms for lipreading"):
  with members:
    - "direct acoustics to word models for english conversational speech recognition"
    - "lipnet end to end sentence level lipreading"
    - "lipreading with long short term memory"
    - "identity mappings in deep residual networks"
    - "lip reading sentences in the wild"
    - "two stage convolutional part heatmap regression for the 1st 3d face alignment in the wild 3dfaw challenge"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "phoneme to viseme mappings"):
  with members:
    - "visual speech recognition aligning terminologies for better understanding"
    - "speaker independent machine lip reading with speaker dependent viseme classifiers"
    - "which phoneme to viseme maps best improve visual only computer lip reading"
    - "some observations on computer lip reading moving from the dream to the reality"
    - "resolution limits on visual speech recognition"
    - "lipreading with long short term memory"
    - "automatic facial expression recognition using features of salient facial patches"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-8 (induced by source paper "vid2speech speech reconstruction from silent video"):
  with members:
    - "lipnet end to end sentence level lipreading"
    - "lipreading with long short term memory"
    - "lip reading sentences in the wild"
    - "visually indicated sounds"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

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)