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: "large scale visual speech recognition"
  Abstract: This work presents a scalable solution to open-vocabulary visual speech recognition. To achieve this, we constructed the largest existing visual speech recognition dataset, consisting of pairs of text and video clips of faces speaking (3,886 hours of video). In tandem, we designed and trained an integrated lipreading system, consisting of a video processing pipeline that maps raw video to stable videos of lips and sequences of phonemes, a scalable deep neural network that maps the lip videos to sequences of phoneme distributions, and a production-level speech decoder that outputs sequences of words. The proposed system achieves a word error rate (WER) of 40.9% as measured on a held-out set. In comparison, professional lipreaders achieve either 86.4% or 92.9% WER on the same dataset when having access to additional types of contextual information. Our approach significantly improves on other lipreading approaches, including variants of LipNet and of Watch, Attend, and Spell (WAS), which are only capable of 89.8% and 76.8% WER respectively.

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"
    - "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"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-2 (induced by source paper "m 3 t multi modal continuous valence arousal estimation in the wild"):
  with members:
    - "facial affect recognition in the wild using multi task learning convolutional network"
    - "analysing affective behavior in the first abaw 2020 competition"
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "retinaface single stage dense face localisation in the wild"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "pytorch an imperative style high performance deep learning library"
    - "a multi task learning generation framework valence arousal action units primary expressions"

Connected through hyperedge HE-3 (induced by source paper "deep audio visual speech recognition"):
  with members:
    - "deep lip reading a comparison of models and an online application"
    - "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"
    - "residual convolutional ctc networks for automatic speech recognition"

Connected through hyperedge HE-4 (induced by source paper "hearing lips improving lip reading by distilling speech recognizers"):
  with members:
    - "a cascade sequence to sequence model for chinese mandarin lip reading"
    - "learning to steer by mimicking features from heterogeneous auxiliary networks"
    - "audio visual speech recognition with a hybrid ctc attention architecture"
    - "smoothgrad removing noise by adding noise"
    - "attention is all you need"
    - "tacotron towards end to end speech synthesis"
    - "aishell 1 an open source mandarin speech corpus and a speech recognition baseline"
    - "combining residual networks with lstms for lipreading"

Connected through hyperedge HE-5 (induced by source paper "asr is all you need cross modal distillation for lip reading"):
  with members:
    - "deep audio visual speech recognition"
    - "guiding ctc posterior spike timings for improved posterior fusion and knowledge distillation"
    - "deep lip reading a comparison of models and an online application"
    - "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"
    - "emotion recognition in speech using cross modal transfer in the wild"
    - "voxceleb2 deep speaker recognition"
    - "audio visual speech recognition with a hybrid ctc attention architecture"

Connected through hyperedge HE-6 (induced by source paper "ava activespeaker an audio visual dataset for active speaker detection"):
  with members:
    - "ava speech a densely labeled dataset of speech activity in movies"
    - "the conversation deep audio visual speech enhancement"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "audio visual speaker diarization based on spatiotemporal bayesian fusion"
    - "audio visual scene analysis with self supervised multisensory features"
    - "automated curriculum learning for neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "visual speech enhancement"

Connected through hyperedge HE-7 (induced by source paper "lipreading with 3d 2d cnn blstm hmm and word ctc models"):
  with members:
    - "deep audio 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"
    - "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-8 (induced by source paper "end to end visual speech recognition for small scale datasets"):
  with members:
    - "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"
    - "lipreading with long short term memory"
    - "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)