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: "channel wise and spatial feature modulation network for single image super resolution"
  Abstract: The performance of single image super-resolution has achieved significant improvement by utilizing deep convolutional neural networks (CNNs). The features in deep CNN contain different types of information which make different contributions to image reconstruction. However, most CNN-based models lack discriminative ability for different types of information and deal with them equally, which results in the representational capacity of the models being limited. On the other hand, as the depth of neural networks grows, the long-term information coming from preceding layers is easy to be weaken or lost in late layers, which is adverse to super-resolving image. To capture more informative features and maintain long-term information for image super-resolution, we propose a channel-wise and spatial feature modulation (CSFM) network in which a sequence of feature-modulation memory (FMM) modules is cascaded with a densely connected structure to transform low-resolution features to high informative features. In each FMM module, we construct a set of channel-wise and spatial attention residual (CSAR) blocks and stack them in a chain structure to dynamically modulate multi-level features in a global-and-local manner. This feature modulation strategy enables the high contribution information to be enhanced and the redundant information to be suppressed. Meanwhile, for long-term information persistence, a gated fusion (GF) node is attached at the end of the FMM module to adaptively fuse hierarchical features and distill more effective information via the dense skip connections and the gating mechanism. Extensive quantitative and qualitative evaluations on benchmark datasets illustrate the superiority of our proposed method over the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "spatial spectral residual network for hyperspectral image super resolution"):
  with members:
    - "locality and structure regularized low rank representation for hyperspectral image classification"
    - "getnet a general end to end 2 d cnn framework for hyperspectral image change detection"
    - "unsupervised sparse dirichlet net for hyperspectral image super resolution"
    - "wide activation for efficient and accurate image super resolution"
    - "residual dense network for image super resolution"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "enhanced deep residual networks for single image super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-2 (induced by source paper "single mr image super resolution via channel splitting and serial fusion network"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "image super resolution using very deep residual channel attention networks"
    - "enhanced deep residual networks for single image super resolution"
    - "memnet a persistent memory network for image restoration"
    - "accelerating the super resolution convolutional neural network"
    - "identity mappings in deep residual networks"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"
    - "accurate image super resolution using very deep convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "ram residual attention module for single image super resolution"):
  with members:
    - "fast and accurate single image super resolution via information distillation network"
    - "image super resolution using very deep residual channel attention networks"
    - "residual dense network for image super resolution"
    - "image super resolution via dual state recurrent networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "sketch based manga retrieval using manga109 dataset"
    - "enhanced deep residual networks for single image super resolution"
    - "memnet a persistent memory 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)