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: "deep audio visual speech recognition"
  Abstract: The goal of this work is to recognise phrases and sentences being spoken by a talking face, with or without the audio. Unlike previous works that have focussed on recognising a limited number of words or phrases, we tackle lip reading as an open-world problem -- unconstrained natural language sentences, and in the wild videos. Our key contributions are: (1) we compare two models for lip reading, one using a CTC loss, and the other using a sequence-to-sequence loss. Both models are built on top of the transformer self-attention architecture; (2) we investigate to what extent lip reading is complementary to audio speech recognition, especially when the audio signal is noisy; (3) we introduce and publicly release two new datasets for audio-visual speech recognition: LRS2-BBC, consisting of thousands of natural sentences from British television; and LRS3-TED, consisting of hundreds of hours of TED and TEDx talks obtained from YouTube. The models that we train surpass the performance of all previous work on lip reading benchmark datasets by a significant margin.

Connected through hyperedge HE-1 (induced by source paper "lipreading using temporal convolutional networks"):
  with members:
    - "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"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-2 (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"
    - "end to end audiovisual 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"
    - "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-3 (induced by source paper "pseudo convolutional policy gradient for sequence to sequence lip reading"):
  with members:
    - "multi grained spatio temporal modeling for lip reading"
    - "deep lip reading a comparison of models and an online application"
    - "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"
    - "lipnet end to end sentence level lipreading"
    - "combining residual networks with lstms for lipreading"
    - "lip reading sentences in the wild"

Connected through hyperedge HE-4 (induced by source paper "learning from videos with deep convolutional lstm networks"):
  with members:
    - "deep lip reading a comparison of models and an online application"
    - "lrs3 ted a large scale dataset for visual speech recognition"
    - "are all languages equally hard to language model"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "revisiting the effectiveness of off the shelf temporal modeling approaches for large scale video classification"
    - "attention is all you need"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "understanding the effective receptive field in deep convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "learning spatio temporal features with two stream deep 3d cnns for lipreading"):
  with members:
    - "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"
    - "hidden two stream convolutional networks for action recognition"

Connected through hyperedge HE-6 (induced by source paper "uncertainty aware audiovisual activity recognition using deep bayesian variational inference"):
  with members:
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "moments in time dataset one million videos for event understanding"
    - "learning to separate object sounds by watching unlabeled video"
    - "evaluating bayesian deep learning methods for semantic segmentation"
    - "audio visual scene analysis with self supervised multisensory features"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "what actions are needed for understanding human actions in videos"

Connected through hyperedge HE-7 (induced by source paper "from speech chain to multimodal chain leveraging cross modal data augmentation for semi supervised learning"):
  with members:
    - "towards robust neural machine translation"
    - "multi scale alignment and contextual history for attention mechanism in sequence to sequence model"
    - "machine speech chain with one shot speaker adaptation"
    - "turbo learning for captionbot and drawingbot"
    - "end to end feedback loss in speech chain framework via straight through estimator"
    - "english conversational telephone speech recognition by humans and machines"
    - "the microsoft 2017 conversational speech recognition system"
    - "end to end audiovisual fusion with lstms"

Connected through hyperedge HE-8 (induced by source paper "towards automatic face to face translation"):
  with members:
    - "a baseline neural machine translation system for indian languages"
    - "massively multilingual neural machine translation"
    - "neural voice cloning with a few samples"
    - "lip movements generation at a glance"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "rapid adaptation of neural machine translation to new languages"
    - "efficiently trainable text to speech system based on deep convolutional networks with guided attention"
    - "attention is all you need"

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)