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: "single image super resolution with dilated convolution based multi scale information learning inception module"
  Abstract: Traditional works have shown that patches in a natural image tend to redundantly recur many times inside the image, both within the same scale, as well as across different scales. Make full use of these multi-scale information can improve the image restoration performance. However, the current proposed deep learning based restoration methods do not take the multi-scale information into account. In this paper, we propose a dilated convolution based inception module to learn multi-scale information and design a deep network for single image super-resolution. Different dilated convolution learns different scale feature, then the inception module concatenates all these features to fuse multi-scale information. In order to increase the reception field of our network to catch more contextual information, we cascade multiple inception modules to constitute a deep network to conduct single image super-resolution. With the novel dilated convolution based inception module, the proposed end-to-end single image super-resolution network can take advantage of multi-scale information to improve image super-resolution performance. Experimental results show that our proposed method outperforms many state-of-the-art single image super-resolution methods.

Connected through hyperedge HE-1 (induced by source paper "near lossless ell_ infty constrained image decompression via deep neural network"):
  with members:
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "generative adversarial networks for extreme learned image compression"
    - "conditional probability models for deep image compression"
    - "deep back projection networks for super resolution"
    - "dilated deep residual network for image denoising"
    - "enhanced deep residual networks for single image super resolution"
    - "deep generative adversarial compression artifact removal"
    - "learning convolutional networks for content weighted image compression"

Connected through hyperedge HE-2 (induced by source paper "brain signal classification via learning connectivity structure"):
  with members:
    - "classification of eeg based brain connectivity networks in schizophrenia using a multi domain connectome convolutional neural network"
    - "a dilated inception network for visual saliency prediction"
    - "learning discrete structures for graph neural networks"
    - "a comprehensive survey on graph neural networks"
    - "convolutional neural network approach for eeg based emotion recognition using brain connectivity and its spatial information"
    - "relational inductive biases deep learning and graph networks"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-3 (induced by source paper "ascnet adaptive scale convolutional neural networks for multi scale feature learning"):
  with members:
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "automated segmentation of cervical nuclei in pap smear images using deformable multi path ensemble model"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "dilated residual networks"
    - "dense transformer networks"
    - "understanding convolution for semantic segmentation"
    - "effective use of dilated convolutions for segmenting small object instances in remote sensing imagery"

Connected through hyperedge HE-4 (induced by source paper "s net a scalable convolutional neural network for jpeg compression artifact reduction"):
  with members:
    - "cas cnn a deep convolutional neural network for image compression artifact suppression"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "enhanced deep residual networks for single image super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "image restoration using convolutional auto encoders with symmetric skip connections"
    - "deep convolution networks for compression artifacts reduction"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "wide residual networks"

Connected through hyperedge HE-5 (induced by source paper "mesonet a compact facial video forgery detection network"):
  with members:
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "xception deep learning with depthwise separable convolutions"
    - "multi scale context aggregation by dilated convolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "humans are easily fooled by digital images"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "3dsrnet video super resolution using 3d convolutional neural networks"):
  with members:
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "real time video super resolution with spatio temporal networks and motion compensation"
    - "accurate image super resolution using very deep convolutional networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "matconvnet convolutional neural networks for matlab"
    - "learning spatiotemporal features with 3d convolutional networks"

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)