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: "deepbinarymask learning a binary mask for video compressive sensing"
  Abstract: In this paper, we propose a novel encoder-decoder neural network model referred to as DeepBinaryMask for video compressive sensing. In video compressive sensing one frame is acquired using a set of coded masks (sensing matrix) from which a number of video frames is reconstructed, equal to the number of coded masks. The proposed framework is an end-to-end model where the sensing matrix is trained along with the video reconstruction. The encoder learns the binary elements of the sensing matrix and the decoder is trained to recover the unknown video sequence. The reconstruction performance is found to improve when using the trained sensing mask from the network as compared to other mask designs such as random, across a wide variety of compressive sensing reconstruction algorithms. Finally, our analysis and discussion offers insights into understanding the characteristics of the trained mask designs that lead to the improved reconstruction quality.

Connected through hyperedge HE-1 (induced by source paper "adaptive image sampling using deep learning and its application on x ray fluorescence image reconstruction"):
  with members:
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "multi modal image processing based on coupled dictionary learning"
    - "deep fully connected networks for video compressive sensing"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "semantic image inpainting with deep generative models"
    - "on demand learning for deep image restoration"
    - "context encoders feature learning by inpainting"
    - "semantic image inpainting with perceptual and contextual losses"

Connected through hyperedge HE-2 (induced by source paper "dr2 net deep residual reconstruction network for image compressive sensing"):
  with members:
    - "deep fully connected networks for video compressive sensing"
    - "compression artifacts removal using convolutional neural networks"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "a deep learning approach to structured signal recovery"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "real time object detection and localization in compressive sensed video on embedded hardware"):
  with members:
    - "a compressive sensing video dataset using pixel wise coded exposure"
    - "yolov3 an incremental improvement"
    - "sniper efficient multi scale training"
    - "csvideonet a real time end to end learning framework for high frame rate video compressive sensing"
    - "youtube 8m a large scale video classification benchmark"
    - "tensorflow a system for large scale machine learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "empirical evaluation of rectified activations in convolutional network"

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)