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 "patch based contour prior image denoising for salt and pepper noise"):
  Abstract: The salt and pepper noise brings a significant challenge to image denoising technology, i.e. how to removal the noise clearly and retain the details effectively? In this paper, we propose a patch-based contour prior denoising approach for salt and pepper noise. First, noisy image is cut into patches as basic representation unit, a discrete total variation model is designed to extract contour structures; Second, a weighted Euclidean distance is designed to search the most similar patches, then, corresponding contour stencils are extracted from these similar patches; At the last, we build filter from contour stencils in the framework of regression. Numerical results illustrate that the proposed method is competitive with the state-of-the-art methods in terms of the peak signal-to-noise (PSNR) and visual effects.

Includes members:
  Member "cycle consistent deep generative hashing for cross modal retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "few shot deep adversarial learning for video based person re identification")
  Member "deep adaptive feature embedding with local sample distributions for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "few shot deep adversarial learning for video based person re identification")
  Member "what and where to match deep spatially multiplicative integration networks for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "few shot deep adversarial learning for video based person re identification")
  Member "multiview spectral clustering via structured low rank matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attribute guided feature learning network for vehicle re identification")
  Member "beyond low rank representations orthogonal clustering basis reconstruction with optimized graph structure for multi view spectral clustering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an introduction to person re identification with generative adversarial networks")
  Member "structured deep hashing with convolutional neural networks for fast person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pac gan an effective pose augmentation scheme for unsupervised cross view person re identification")
  Member "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a bert based sentiment analysis and key entity detection approach for online financial texts")

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.MM(Multimedia)