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: "csvideonet a real time end to end learning framework for high frame rate video compressive sensing"
  Abstract: This paper addresses the real-time encoding-decoding problem for high-frame-rate video compressive sensing (CS). Unlike prior works that perform reconstruction using iterative optimization-based approaches, we propose a non-iterative model, named "CSVideoNet". CSVideoNet directly learns the inverse mapping of CS and reconstructs the original input in a single forward propagation. To overcome the limitations of existing CS cameras, we propose a multi-rate CNN and a synthesizing RNN to improve the trade-off between compression ratio (CR) and spatial-temporal resolution of the reconstructed videos. The experiment results demonstrate that CSVideoNet significantly outperforms the state-of-the-art approaches. With no pre/post-processing, we achieve 25dB PSNR recovery quality at 100x CR, with a frame rate of 125 fps on a Titan X GPU. Due to the feedforward and high-data-concurrency natures of CSVideoNet, it can take advantage of GPU acceleration to achieve three orders of magnitude speed-up over conventional iterative-based approaches. We share the source code at this https URL

Connected through hyperedge HE-1 (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"
    - "deepbinarymask learning a binary mask for 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"

Connected through hyperedge HE-2 (induced by source paper "snapshot compressed sensing performance bounds and algorithms"):
  with members:
    - "rank minimization for snapshot compressive imaging"
    - "an efficient algorithm for compression based compressed sensing"
    - "deep fully connected networks for video compressive sensing"
    - "generalized alternating projection based total variation minimization for compressive sensing"
    - "compressed sensing with structured sparsity and structured acquisition"
    - "video compressive sensing for spatial multiplexing cameras using motion flow models"
    - "compressive hyperspectral imaging with side information"
    - "from denoising to compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "deep learning based csi feedback approach for time varying massive mimo channels"):
  with members:
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "deep learning for wireless physical layer opportunities and challenges"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "deep learning for massive mimo csi feedback"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "from denoising to compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "rate adaptive neural networks for spatial multiplexers"):
  with members:
    - "deep fully connected networks for video compressive sensing"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "a deep learning approach to structured signal recovery"

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)