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: "motion fused frames data level fusion strategy for hand gesture recognition"
  Abstract: Acquiring spatio-temporal states of an action is the most crucial step for action classification. In this paper, we propose a data level fusion strategy, Motion Fused Frames (MFFs), designed to fuse motion information into static images as better representatives of spatio-temporal states of an action. MFFs can be used as input to any deep learning architecture with very little modification on the network. We evaluate MFFs on hand gesture recognition tasks using three video datasets - Jester, ChaLearn LAP IsoGD and NVIDIA Dynamic Hand Gesture Datasets - which require capturing long-term temporal relations of hand movements. Our approach obtains very competitive performance on Jester and ChaLearn benchmarks with the classification accuracies of 96.28% and 57.4%, respectively, while achieving state-of-the-art performance with 84.7% accuracy on NVIDIA benchmark.

Connected through hyperedge HE-1 (induced by source paper "learning video representations from correspondence proposals"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "end to end learning of motion representation for video understanding"
    - "flownet3d learning scene flow in 3d point clouds"
    - "motion feature network fixed motion filter for action recognition"
    - "deep sets"
    - "dynamic graph cnn for learning on point clouds"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "mmtm multimodal transfer module for cnn fusion"):
  with members:
    - "my lips are concealed audio visual speech enhancement through obstructions"
    - "mfas multimodal fusion architecture search"
    - "the conversation deep audio visual speech enhancement"
    - "centralnet a multilayer approach for multimodal fusion"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "improving the performance of unimodal dynamic hand gesture recognition with multimodal training"

Connected through hyperedge HE-3 (induced by source paper "comparative analysis of cnn based spatiotemporal reasoning in videos"):
  with members:
    - "resource efficient 3d convolutional neural networks"
    - "multi level wavelet cnn for image restoration"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "a simple neural network module for relational reasoning"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "the something something video database for learning and evaluating visual common sense"
    - "temporal relational reasoning in videos"

Connected through hyperedge HE-4 (induced by source paper "talking with your hands scaling hand gestures and recognition with cnns"):
  with members:
    - "real time hand gesture detection and classification using convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "temporal segment networks towards good practices for deep action recognition"

Connected through hyperedge HE-5 (induced by source paper "real time hand gesture detection and classification using convolutional neural networks"):
  with members:
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "convnet architecture search for spatiotemporal feature learning"
    - "temporal segment networks towards good practices for deep action recognition"
    - "deep residual learning for image recognition"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "two stream convolutional networks for action recognition in videos"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "real time driver state monitoring using a cnn based spatio temporal approach"):
  with members:
    - "real time distracted driver posture classification"
    - "brain4cars car that knows before you do via sensory fusion deep learning architecture"
    - "car that knows before you do anticipating maneuvers via learning temporal driving models"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

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)