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 relaxation of nonconvex energies"
  Abstract: We propose a novel spatially continuous framework for convex relaxations based on functional lifting. Our method can be interpreted as a sublabel-accurate solution to multilabel problems. We show that previously proposed functional lifting methods optimize an energy which is linear between two labels and hence require (often infinitely) many labels for a faithful approximation. In contrast, the proposed formulation is based on a piecewise convex approximation and therefore needs far fewer labels. In comparison to recent MRF-based approaches, our method is formulated in a spatially continuous setting and shows less grid bias. Moreover, in a local sense, our formulation is the tightest possible convex relaxation. It is easy to implement and allows an efficient primal-dual optimization on GPUs. We show the effectiveness of our approach on several computer vision problems.

Connected through hyperedge HE-1 (induced by source paper "ultrametric fitting by gradient descent"):
  with members:
    - "on the convergence of adam and beyond"
    - "reliable agglomerative clustering"
    - "hierarchical graph clustering using node pair sampling"
    - "hierarchical clustering with structural constraints"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "interactive bayesian hierarchical clustering"
    - "hierarchical clustering via spreading metrics"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"

Connected through hyperedge HE-2 (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 convex relaxation of vectorial multilabel energies"
    - "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"

Connected through hyperedge HE-3 (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"
    - "sublabel accurate convex relaxation of vectorial multilabel energies"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "on learning optimized reaction diffusion processes for effective image restoration"
    - "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-4 (induced by source paper "end to end training of hybrid cnn crf models for stereo"):
  with members:
    - "sublabel accurate convex relaxation of vectorial multilabel energies"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "a combinatorial solution to non rigid 3d shape to image matching"):
  with members:
    - "sublabel accurate convex relaxation of vectorial multilabel energies"
    - "efficient globally optimal 2d to 3d deformable shape matching"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "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:
    - "sublabel accurate convex relaxation of vectorial multilabel energies"
    - "submodular functions from discrete to continous domains"
    - "continuous multiclass labeling approaches and algorithms"

Connected through hyperedge HE-7 (induced by source paper "multiframe motion coupling for video super resolution"):
  with members:
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "fast free adaptive super resolution via transfer for compressed videos"
    - "accurate image super resolution using very deep convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "precise relaxation of the mumford shah functional"):
  with members:
    - "sublabel accurate convex relaxation of vectorial multilabel energies"
    - "submodular functions from discrete to continous domains"
    - "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)