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: "con patch when a patch meets its context"
  Abstract: Measuring the similarity between the patches in images is a fundamental building block in various tasks. Naturally, the patch size has a major impact on the matching quality and on the consequent application performance. Under the assumption that our patch database is sufficiently sampled, using large patches (e.g.,    $21 \,\, \times \,\, 21$   ) should be preferred over small ones (e.g.,    $7 \,\, \times \,\, 7$    ). However, this dense-sampling assumption is rarely true; in most cases, large patches cannot find relevant nearby examples. This phenomenon is a consequence of the curse of dimensionality, stating that the database size should grow exponentially with the patch size to ensure proper matches. This explains the favored choice of small patch size in most applications. Is there a way to keep the simplicity and work with small patches while getting some of the benefits that large patches provide? In this paper, we offer such an approach. We propose to concatenate the regular content of a conventional (small) patch with a compact representation of its (large) surroundings—its context. Therefore, with a minor increase of the dimensions (e.g., with additional ten values to the patch representation), we implicitly/softly describe the information of a large patch. The additional descriptors are computed based on a self-similarity behavior of the patch surrounding. We show that this approach achieves better matches, compared with the use of conventional-size patches, without the need to increase the database-size. Also, the effectiveness of the proposed method is tested on three distinct problems: 1) external natural image denoising; 2) depth image super-resolution; and 3) motion-compensated frame-rate up conversion.

Connected through hyperedge HE-1 (induced by source paper "context patch face hallucination based on thresholding locality constrained representation and reproducing learning"):
  with members:
    - "fsrnet end to end learning face super resolution with facial priors"
    - "attention aware face hallucination via deep reinforcement learning"
    - "learning to hallucinate face images via component generation and enhancement"
    - "deep cascaded bi network for face hallucination"
    - "face hallucination using linear models of coupled sparse support"
    - "deep networks for image super resolution with sparse prior"
    - "image super resolution using deep convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "example based image synthesis via randomized patch matching"):
  with members:
    - "perceptual losses for real time style transfer and super resolution"
    - "combining markov random fields and convolutional neural networks for image synthesis"
    - "image to image translation with conditional adversarial networks"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "pixel recurrent neural networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "texture synthesis using convolutional neural networks"
    - "draw a recurrent neural network for image generation"

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)