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: "sublabel accurate convex relaxation of vectorial multilabel energies"
  Abstract: Convex relaxations of nonconvex multilabel problems have been demonstrated to produce superior (provably optimal or near-optimal) solutions to a variety of classical computer vision problems. Yet, they are of limited practical use as they require a fine discretization of the label space, entailing a huge demand in memory and runtime. In this work, we propose the first sublabel accurate convex relaxation for vectorial multilabel problems. The key idea is that we approximate the dataterm of the vectorial labeling problem in a piecewise convex (rather than piecewise linear) manner. As a result we have a more faithful approximation of the original cost function that provides a meaningful interpretation for the fractional solutions of the relaxed convex problem. In numerous experiments on large-displacement optical flow estimation and on color image denoising we demonstrate that the computed solutions have superior quality while requiring much lower memory and runtime.

Connected through hyperedge HE-1 (induced by source paper "lifting vectorial variational problems a natural formulation based on geometric measure theory and discrete exterior calculus"):
  with members:
    - "functional maps representation on product manifolds"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "sublabel accurate discretization of nonconvex free discontinuity problems"
    - "full flow optical flow estimation by global optimization over regular grids"
    - "complexity of discrete energy minimization problems"
    - "sublabel accurate relaxation of nonconvex energies"

Connected through hyperedge HE-2 (induced by source paper "lifting layers analysis and applications"):
  with members:
    - "self normalizing neural networks"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "on learning optimized reaction diffusion processes for effective image restoration"
    - "sublabel accurate relaxation of nonconvex energies"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-3 (induced by source paper "multi label segmentation via residual driven adaptive regularization"):
  with members:
    - "interactive image segmentation using constrained dominant sets"
    - "continuous multiclass labeling approaches and algorithms"

Connected through hyperedge HE-4 (induced by source paper "end to end training of hybrid cnn crf models for stereo"):
  with members:
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "complexity of discrete energy minimization problems"
    - "solving dense image matching in real time using discrete continuous optimization"
    - "semantic image segmentation via deep parsing network"
    - "computing the stereo matching cost with a convolutional neural network"
    - "efficient likelihood learning of a generic cnn crf model for semantic segmentation"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "conditional random fields as recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "a combinatorial solution to non rigid 3d shape to image matching"):
  with members:
    - "efficient globally optimal 2d to 3d deformable shape matching"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "sublabel accurate relaxation of nonconvex energies"
    - "globally optimal joint image segmentation and shape matching based on wasserstein modes"
    - "functional correspondence by matrix completion"
    - "continuous multiclass labeling approaches and algorithms"
    - "point set registration coherent point drift"

Connected through hyperedge HE-6 (induced by source paper "sublabel accurate discretization of nonconvex free discontinuity problems"):
  with members:
    - "submodular functions from discrete to continous domains"
    - "sublabel accurate relaxation of nonconvex energies"
    - "continuous multiclass labeling approaches and algorithms"

Connected through hyperedge HE-7 (induced by source paper "precise relaxation of the mumford shah functional"):
  with members:
    - "submodular functions from discrete to continous domains"
    - "sublabel accurate relaxation of nonconvex energies"
    - "continuous multiclass labeling approaches and algorithms"

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)