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 lip reading a comparison of models and an online application"
  Abstract: The goal of this paper is to develop state-of-the-art models for lip reading -- visual speech recognition. We develop three architectures and compare their accuracy and training times: (i) a recurrent model using LSTMs; (ii) a fully convolutional model; and (iii) the recently proposed transformer model. The recurrent and fully convolutional models are trained with a Connectionist Temporal Classification loss and use an explicit language model for decoding, the transformer is a sequence-to-sequence model. Our best performing model improves the state-of-the-art word error rate on the challenging BBC-Oxford Lip Reading Sentences 2 (LRS2) benchmark dataset by over 20 percent. #R##N#As a further contribution we investigate the fully convolutional model when used for online (real time) lip reading of continuous speech, and show that it achieves high performance with low latency.

Connected through hyperedge HE-1 (induced by source paper "pseudo convolutional policy gradient for sequence to sequence lip reading"):
  with members:
    - "deep audio visual speech recognition"
    - "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"
    - "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-2 (induced by source paper "predicting 3d human dynamics from video"):
  with members:
    - "xnect real time multi person 3d human pose estimation with a single rgb camera"
    - "exploiting temporal context for 3d human pose estimation in the wild"
    - "sim2real transfer learning for 3d pose estimation motion to the rescue"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "learning 3d human dynamics from video"
    - "flow grounded spatial temporal video prediction from still images"

Connected through hyperedge HE-3 (induced by source paper "deep audio visual speech recognition"):
  with members:
    - "large scale visual speech recognition"
    - "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 "learning from videos with deep convolutional lstm networks"):
  with members:
    - "deep audio visual speech recognition"
    - "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 "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"
    - "large scale visual speech recognition"
    - "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 "learning 3d human dynamics from video"):
  with members:
    - "reticam real time human performance capture from monocular video"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "bodynet volumetric inference of 3d human body shapes"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "deep autoencoder for combined human pose estimation and body model upscaling"
    - "video based reconstruction of 3d people models"

Connected through hyperedge HE-7 (induced by source paper "zero shot keyword spotting for visual speech recognition in the wild"):
  with members:
    - "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"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"

Connected through hyperedge HE-8 (induced by source paper "pushing the boundaries of audiovisual word recognition using residual networks and lstms"):
  with members:
    - "the conversation deep audio visual speech enhancement"
    - "advancing acoustic to word ctc model"
    - "end to end audiovisual speech recognition"
    - "zero shot keyword spotting for visual speech recognition in the wild"
    - "improving speaker independent lipreading with domain adversarial training"
    - "attention is all you need"
    - "deep word embeddings for visual speech recognition"
    - "end to end audiovisual fusion with lstms"

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)