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: "revisiting loss specific training of filter based mrfs for image restoration"
  Abstract: It is now well known that Markov random fields (MRFs) are particularly effective for modeling image priors in low-level vision. Recent years have seen the emergence of two main approaches for learning the parameters in MRFs: (1) probabilistic learning using sampling-based algorithms and (2) loss-specific training based on MAP estimate. After investigating existing training approaches, it turns out that the performance of the loss-specific training has been significantly underestimated in existing work. In this paper, we revisit this approach and use techniques from bi-level optimization to solve it. We show that we can get a substantial gain in the final performance by solving the lower-level problem in the bi-level framework with high accuracy using our newly proposed algorithm. As a result, our trained model is on par with highly specialized image denoising algorithms and clearly outperforms probabilistically trained MRF models. Our findings suggest that for the loss-specific training scheme, solving the lower-level problem with higher accuracy is beneficial. Our trained model comes along with the additional advantage, that inference is extremely efficient. Our GPU-based implementation takes less than 1s to produce state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"):
  with members:
    - "on learning optimized reaction diffusion processes for effective image restoration"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "sparsity based poisson denoising with dictionary learning"
    - "insights into analysis operator learning from patch based sparse models to higher order mrfs"
    - "learning activation functions to improve deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "generic 3d convolutional fusion for image restoration"):
  with members:
    - "cascades of regression tree fields for image restoration"
    - "seven ways to improve example based single image super resolution"
    - "accurate image super resolution using very deep convolutional networks"
    - "deeply recursive convolutional network for image super resolution"
    - "on learning optimized reaction diffusion processes for effective image restoration"
    - "deep networks for image super resolution with sparse prior"
    - "adam a method for stochastic optimization"
    - "image super resolution using deep convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "deep gaussian conditional random field network a model based deep network for discriminative denoising"):
  with members:
    - "cascades of regression tree fields for image restoration"
    - "conditional random fields as recurrent neural networks"
    - "fully connected deep structured networks"
    - "on learning optimized reaction diffusion processes for effective image restoration"
    - "deep unfolding model based inspiration of novel deep architectures"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "on learning optimized reaction diffusion processes for effective image restoration"):
  with members:
    - "insights into analysis operator learning from patch based sparse models to higher order mrfs"

Connected through hyperedge HE-5 (induced by source paper "insights into analysis operator learning from patch based sparse models to higher order mrfs"):
  with members:
    - "learning ell_1 based analysis and synthesis sparsity priors using bi level optimization"
    - "analysis operator learning and its application to image reconstruction"

Connected through hyperedge HE-6 (induced by source paper "a bi level view of inpainting based image compression"):
  with members:
    - "insights into analysis operator learning from patch based sparse models to higher order mrfs"

Connected through hyperedge HE-7 (induced by source paper "a higher order mrf based variational model for multiplicative noise reduction"):
  with members:
    - "insights into analysis operator learning from patch based sparse models to higher order mrfs"

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)