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 "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.

Includes members:
  Member "trainlets dictionary learning in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dictionary learning for adaptive gpr landmine classification")
  Member "adaptive image denoising by targeted databases":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "self supervised fast adaptation for denoising via meta learning")
  Member "boosting of image denoising algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep k svd denoising")
  Member "motion compensated coding and frame rate up conversion models and analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimized pre compensating compression")

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)