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: "image smoothing via unsupervised learning"
  Abstract: Image smoothing represents a fundamental component of many disparate computer vision and graphics applications. In this paper, we present a unified unsupervised (label-free) learning framework that facilitates generating flexible and high-quality smoothing effects by directly learning from data using deep convolutional neural networks (CNNs). The heart of the design is the training signal as a novel energy function that includes an edge-preserving regularizer which helps maintain important yet potentially vulnerable image structures, and a spatially-adaptive Lp flattening criterion which imposes different forms of regularization onto different image regions for better smoothing quality. We implement a diverse set of image smoothing solutions employing the unified framework targeting various applications such as, image abstraction, pencil sketching, detail enhancement, texture removal and content-aware image manipulation, and obtain results comparable with or better than previous methods. Moreover, our method is extremely fast with a modern GPU (e.g, 200 fps for 1280x720 images). Our codes and model are released in this https URL.

Connected through hyperedge HE-1 (induced by source paper "edgefool an adversarial image enhancement filter"):
  with members:
    - "big but imperceptible adversarial perturbations via semantic manipulation"
    - "semantic adversarial examples"
    - "fast end to end trainable guided filter"
    - "sparsefool a few pixels make a big difference"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "a generalized framework for edge preserving and structure preserving image smoothing"):
  with members:
    - "a general decoupled learning framework for parameterized image operators"
    - "fast image processing with fully convolutional networks"
    - "the fast bilateral solver"

Connected through hyperedge HE-3 (induced by source paper "deep energy unsupervised training of deep neural networks"):
  with members:
    - "gated fusion network for single image dehazing"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "deep image prior"
    - "deep image matting"
    - "benchmarking single image dehazing and beyond"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "fast image processing with fully convolutional networks"

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)