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: "continuous multiclass labeling approaches and algorithms"
  Abstract: We study convex relaxations of the image labeling problem on a continuous domain with regularizers based on metric interaction potentials. The generic framework ensures existence of minimizers and covers a wide range of relaxations of the originally combinatorial problem. We focus on two specific relaxations that differ in flexibility and simplicity -- one can be used to tightly relax any metric interaction potential, while the other one only covers Euclidean metrics but requires less computational effort. For solving the nonsmooth discretized problem, we propose a globally convergent Douglas-Rachford scheme, and show that a sequence of dual iterates can be recovered in order to provide a posteriori optimality bounds. In a quantitative comparison to two other first-order methods, the approach shows competitive performance on synthetical and real-world images. By combining the method with an improved binarization technique for nonstandard potentials, we were able to routinely recover discrete solutions within 1%--5% of the global optimum for the combinatorial image labeling problem.

Connected through hyperedge HE-1 (induced by source paper "image labeling based on graphical models using wasserstein messages and geometric assignment"):
  with members:
    - "opengm a c library for discrete graphical models"
    - "norm product belief propagation primal dual message passing for approximate inference"

Connected through hyperedge HE-2 (induced by source paper "a discriminative view of mrf pre processing algorithms"):
  with members:
    - "full flow optical flow estimation by global optimization over regular grids"
    - "maximum persistency via iterative relaxed inference with graphical models"
    - "maximum persistency in energy minimization"
    - "partial optimality by pruning for map inference with general graphical models"
    - "tightening lp relaxations for map using message passing"
    - "efficiently searching for frustrated cycles in map inference"
    - "generalized roof duality and bisubmodular functions"

Connected through hyperedge HE-3 (induced by source paper "image labeling by assignment"):
  with members:
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "partial optimality by pruning for map inference with general graphical models"
    - "optimality bounds for a variational relaxation of the image partitioning problem"

Connected through hyperedge HE-4 (induced by source paper "adaptive regularization of some inverse problems in image analysis"):
  with members:
    - "learning raw image denoising with bayer pattern unification and bayer preserving augmentation"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "interactive image segmentation using constrained dominant sets"

Connected through hyperedge HE-5 (induced by source paper "multi label segmentation via residual driven adaptive regularization"):
  with members:
    - "sublabel accurate convex relaxation of vectorial multilabel energies"
    - "interactive image segmentation using constrained dominant sets"

Connected through hyperedge HE-6 (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"
    - "sublabel accurate relaxation of nonconvex energies"
    - "globally optimal joint image segmentation and shape matching based on wasserstein modes"
    - "functional correspondence by matrix completion"
    - "point set registration coherent point drift"

Connected through hyperedge HE-7 (induced by source paper "multicuts and perturb map for probabilistic graph clustering"):
  with members:
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "higher order segmentation via multicuts"
    - "on sampling from the gibbs distribution with random maximum a posteriori perturbations"
    - "opengm a c library for discrete graphical models"
    - "on the partition function and random maximum a posteriori perturbations"
    - "large scale correlation clustering optimization"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-8 (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"
    - "sublabel accurate relaxation of nonconvex energies"

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)