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: "learning spatio temporal features with two stream deep 3d cnns for lipreading"
  Abstract: We focus on the word-level visual lipreading, which requires recognizing the word being spoken, given only the video but not the audio. State-of-the-art methods explore the use of end-to-end neural networks, including a shallow (up to three layers) 3D convolutional neural network (CNN) + a deep 2D CNN (\emph{e.g.}, ResNet) as the front-end to extract visual features, and a recurrent neural network (\emph{e.g.}, bidirectional LSTM) as the back-end for classification. In this work, we propose to replace the shallow 3D CNNs + deep 2D CNNs front-end with recent successful deep 3D CNNs --- two-stream (\emph{i.e.}, grayscale video and optical flow streams) I3D. We evaluate different combinations of front-end and back-end modules with the grayscale video and optical flow inputs on the LRW dataset. The experiments show that, compared to the shallow 3D CNNs + deep 2D CNNs front-end, the deep 3D CNNs front-end with pre-training on the large-scale image and video datasets (\emph{e.g.}, ImageNet and Kinetics) can improve the classification accuracy. On the other hand, we demonstrate that using the optical flow input alone can achieve comparable performance as using the grayscale video as input. Moreover, the two-stream network using both the grayscale video and optical flow inputs can further improve the performance. Overall, our two-stream I3D front-end with a Bi-LSTM back-end results in an absolute improvement of 5.3\% over the previous art.

Connected through hyperedge HE-1 (induced by source paper "lipreading using temporal convolutional networks"):
  with members:
    - "deep audio visual speech recognition"
    - "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 "deformation flow based two stream network for lip reading"):
  with members:
    - "multi grained spatio temporal modeling for lip reading"
    - "self supervised learning of a facial attribute embedding from video"
    - "lrw 1000 a naturally distributed large scale benchmark for lip reading in the wild"
    - "on the integration of optical flow and action recognition"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "lipnet end to end sentence level lipreading"
    - "combining residual networks with lstms for lipreading"
    - "lip reading sentences in the wild"

Connected through hyperedge HE-3 (induced by source paper "forecasting time to collision from monocular video feasibility dataset and challenges"):
  with members:
    - "a baseline for 3d multi object tracking"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "a visual neural network for robust collision perception in vehicle driving scenarios"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "yolov3 an incremental improvement"
    - "anticipating traffic accidents with adaptive loss and large scale incident db"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "learning spatio temporal representation with pseudo 3d residual networks"

Connected through hyperedge HE-4 (induced by source paper "future near collision prediction from monocular video feasibility dataset and challenges"):
  with members:
    - "a baseline for 3d multi object tracking"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "a visual neural network for robust collision perception in vehicle driving scenarios"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "an evaluation of trajectory prediction approaches and notes on the trajnet benchmark"
    - "yolov3 an incremental improvement"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "learning spatio temporal representation with pseudo 3d residual networks"

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)