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: "a bi level view of inpainting based image compression"
  Abstract: Inpainting based image compression approaches, especially linear and non-linear diffusion models, are an active research topic for lossy image compression. The major challenge in these compression models is to find a small set of descriptive supporting points, which allow for an accurate reconstruction of the original image. It turns out in practice that this is a challenging problem even for the simplest Laplacian interpolation model. In this paper, we revisit the Laplacian interpolation compression model and introduce two fast algorithms, namely successive preconditioning primal dual algorithm and the recently proposed iPiano algorithm, to solve this problem efficiently. Furthermore, we extend the Laplacian interpolation based compression model to a more general form, which is based on principles from bi-level optimization. We investigate two different variants of the Laplacian model, namely biharmonic interpolation and smoothed Total Variation regularization. Our numerical results show that significant improvements can be obtained from the biharmonic interpolation model, and it can recover an image with very high quality from only 5% pixels.

Connected through hyperedge HE-1 (induced by source paper "learning energy based inpainting for optical flow"):
  with members:
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "scalable full flow with learned binary descriptors"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "optical flow estimation using a spatial pyramid network"
    - "interponet a brain inspired neural network for optical flow dense interpolation"
    - "training deep nets with sublinear memory cost"
    - "theano a python framework for fast computation of mathematical expressions"

Connected through hyperedge HE-2 (induced by source paper "visualizing deep convolutional neural networks using natural pre images"):
  with members:
    - "understanding neural networks through deep visualization"
    - "a neural algorithm of artistic style"
    - "texture synthesis using convolutional neural networks"
    - "image reconstruction from bag of visual words"
    - "understanding deep image representations by inverting them"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "return of the devil in the details delving deep into convolutional nets"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "optimising spatial and tonal data for pde based inpainting"):
  with members:
    - "anisotropic mesh adaptation for image representation and scaling"
    - "from bits to images inversion of local binary descriptors"

Connected through hyperedge HE-4 (induced by source paper "understanding deep image representations by inverting them"):
  with members:
    - "image reconstruction from bag of visual words"
    - "imagenet large scale visual recognition challenge"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "visualizing and understanding convolutional networks"
    - "deep inside convolutional networks visualising image classification models and saliency maps"
    - "intriguing properties of neural 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)