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 "difficulty aware image super resolution via deep adaptive dual network"):
  Abstract: Recently, deep learning based single image super-resolution(SR) approaches have achieved great development. The state-of-the-art SR methods usually adopt a feed-forward pipeline to establish a non-linear mapping between low-res(LR) and high-res(HR) images. However, due to treating all image regions equally without considering the difficulty diversity, these approaches meet an upper bound for optimization. To address this issue, we propose a novel SR approach that discriminately processes each image region within an image by its difficulty. Specifically, we propose a dual-way SR network that one way is trained to focus on easy image regions and another is trained to handle hard image regions. To identify whether a region is easy or hard, we propose a novel image difficulty recognition network based on PSNR prior. Our SR approach that uses the region mask to adaptively enforce the dual-way SR network yields superior results. Extensive experiments on several standard benchmarks (e.g., Set5, Set14, BSD100, and Urban100) show that our approach achieves state-of-the-art performance.

Includes members:
  Member "cross modal attentional context learning for rgb d object detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "pirm challenge on perceptual image enhancement on smartphones report":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ddet dual path dynamic enhancement network for real world image super resolution")
  Member "fast and accurate single image super resolution via information distillation network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "multi level wavelet cnn for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning based video coding a review and a case study")
  Member "attention aware face hallucination via deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "face hallucination with finishing touches")
  Member "structure preserving image super resolution via contextualized multitask learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ddet dual path dynamic enhancement network for real world 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)