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 "basis prediction networks for effective burst denoising with large kernels"):
  Abstract: Bursts of images exhibit significant self-similarity across both time and space. This motivates a representation of the kernels as linear combinations of a small set of basis elements. To this end, we introduce a novel basis prediction network that, given an input burst, predicts a set of global basis kernels --- shared within the image --- and the corresponding mixing coefficients --- which are specific to individual pixels. Compared to other state-of-the-art deep learning techniques that output a large tensor of per-pixel spatiotemporal kernels, our formulation substantially reduces the dimensionality of the network output. This allows us to effectively exploit larger denoising kernels and achieve significant quality improvements (over 1dB PSNR) at reduced run-times compared to state-of-the-art methods.

Includes members:
  Member "residual non local attention networks for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "multi kernel prediction networks for denoising of burst images":
    also connected through other hyperedges:
      (none)
  Member "handheld mobile photography in very low light":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "burst denoising of dark images")
  Member "iterative residual cnns for burst photography applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transfer learning from synthetic to real noise denoising with adaptive instance normalization")
  Member "identifying recurring patterns with deep neural networks for natural image denoising":
    also connected through other hyperedges:
      (none)
  Member "non local recurrent network for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fine grained image to image transformation towards visual recognition")
  Member "learning to see in the dark":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "blade filter learning for general purpose computational photography":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "image stylization from predefined to personalized")

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)