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 "ram residual attention module for single image super resolution"):
  Abstract: Attention mechanisms are a design trend of deep neural networks that stands out in various computer vision tasks. Recently, some works have attempted to apply attention mechanisms to single image super-resolution (SR) tasks. However, they apply the mechanisms to SR in the same or similar ways used for high-level computer vision problems without much consideration of the different nature between SR and other problems. In this paper, we propose a new attention method, which is composed of new channel-wise and spatial attention mechanisms optimized for SR and a new fused attention to combine them. Based on this, we propose a new residual attention module (RAM) and a SR network using RAM (SRRAM). We provide in-depth experimental analysis of different attention mechanisms in SR. It is shown that the proposed method can construct both deep and lightweight SR networks showing improved performance in comparison to existing state-of-the-art methods.

Includes members:
  Member "fast and accurate single image super resolution via information distillation network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "image super resolution using very deep residual channel attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "channel wise and spatial feature modulation network for single image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spatial spectral residual network for hyperspectral image super resolution")
  Member "residual dense network for image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "seeing through fog without seeing fog deep multimodal sensor fusion in unseen adverse weather")
  Member "image super resolution via dual state recurrent networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust regression via deep negative correlation learning")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "sketch based manga retrieval using manga109 dataset":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "closed loop matters dual regression networks for single image super resolution")
  Member "enhanced deep residual networks for single image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning enriched features for real image restoration and enhancement")

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)