human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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

Includes members:
  Member "deeprebirth accelerating deep neural network execution on mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "flame a self adaptive auto labeling system for heterogeneous mobile processors")
  Member "deep fully connected networks for video compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive image sampling using deep learning and its application on x ray fluorescence image reconstruction")
  Member "reconnet non iterative reconstruction of images from compressively sensed random measurements":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ada lista learned solvers adaptive to varying models")
  Member "unsupervised learning of video representations using lstms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "a deep learning approach to structured signal recovery":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "inference with deep generative priors in high dimensions")
  Member "image question answering using convolutional neural network with dynamic parameter prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust explanations for visual question answering")
  Member "action recognition using visual attention":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "movi a large multipurpose motion and video dataset")
  Member "stacked attention networks for image question answering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ha ccn hierarchical attention based crowd counting network")

Question: Please tell me which class the source paper 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)