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: "lightweight feature fusion network for single image super resolution"
  Abstract: Single image super-resolution (SISR) has witnessed great progress as convolutional neural network (CNN) gets deeper and wider. However, enormous parameters hinder its application to real world problems. In this letter, We propose a lightweight feature fusion network (LFFN) that can fully explore multi-scale contextual information and greatly reduce network parameters while maximizing SISR results. LFFN is built on spindle blocks and a softmax feature fusion module (SFFM). Specifically, a spindle block is composed of a dimension extension unit, a feature exploration unit. and a feature refinement unit. The dimension extension layer expands low dimension to high dimension and implicitly learns the feature maps which are suitable for the next unit. The feature exploration unit performs linear and nonlinear feature exploration aimed at different feature maps. The feature refinement layer is used to fuse and refine features. SFFM fuses the features from different modules in a self-adaptive learning manner with softmax function, making full use of hierarchical information with a small amount of parameter cost. Both qualitative and quantitative experiments on benchmark datasets show that LFFN achieves favorable performance against state-of-the-art methods with similar parameters.

Connected through hyperedge HE-1 (induced by source paper "hierarchical neural architecture search for single image super resolution"):
  with members:
    - "discrimination aware network pruning for deep model compression"
    - "fast accurate and lightweight super resolution with neural architecture search"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "multi objective reinforced evolution in mobile neural architecture search"
    - "nat neural architecture transformer for accurate and compact architectures"
    - "adversarial learning with local coordinate coding"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "dual reconstruction nets for image super resolution with gradient sensitive loss"

Connected through hyperedge HE-2 (induced by source paper "ddet dual path dynamic enhancement network for real world image super resolution"):
  with members:
    - "toward real world single image super resolution a new benchmark and a new model"
    - "super resolution via image adapted denoising cnns incorporating external and internal learning"
    - "pirm challenge on perceptual image enhancement on smartphones report"
    - "attention aware face hallucination via deep reinforcement learning"
    - "structure preserving image super resolution via contextualized multitask learning"
    - "enhanced deep residual networks for single image super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "dynamic filter networks"

Connected through hyperedge HE-3 (induced by source paper "cfsnet toward a controllable feature space for image restoration"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "maintaining natural image statistics with the contextual loss"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "toward convolutional blind denoising of real photographs"
    - "the perception distortion tradeoff"
    - "non local recurrent network for image restoration"

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)