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 linear framework for region based image segmentation and inpainting involving curvature penalization"
  Abstract: We present the first method to handle curvature regularity in region-based image segmentation and inpainting that is independent of initialization. #R##N#To this end we start from a new formulation of length-based optimization schemes, based on surface continuation constraints, and discuss the connections to existing schemes. The formulation is based on a \emph{cell complex} and considers basic regions and boundary elements. The corresponding optimization problem is cast as an integer linear program. #R##N#We then show how the method can be extended to include curvature regularity, again cast as an integer linear program. Here, we are considering pairs of boundary elements to reflect curvature. Moreover, a constraint set is derived to ensure that the boundary variables indeed reflect the boundary of the regions described by the region variables. #R##N#We show that by solving the linear programming relaxation one gets quite close to the global optimum, and that curvature regularity is indeed much better suited in the presence of long and thin objects compared to standard length regularity.

Connected through hyperedge HE-1 (induced by source paper "curvature regularized surface reconstruction from point cloud"):
  with members:
    - "weighted mean curvature"

Connected through hyperedge HE-2 (induced by source paper "divergence prior and vessel tree reconstruction"):
  with members:
    - "on regularized losses for weakly supervised cnn segmentation"
    - "thin structure estimation with curvature regularization"

Connected through hyperedge HE-3 (induced by source paper "efficient regularization of squared curvature"):
  with members:
    - "submodularization for quadratic pseudo boolean optimization"
    - "curvature prior for mrf based segmentation and shape inpainting"

Connected through hyperedge HE-4 (induced by source paper "generalized sequential tree reweighted message passing"):
  with members:
    - "convergent message passing algorithms a unifying view"
    - "tightening lp relaxations for map using message passing"
    - "norm product belief propagation primal dual message passing for approximate inference"

Connected through hyperedge HE-5 (induced by source paper "curvature prior for mrf based segmentation and shape inpainting"):
  with members:
    - "flexible priors for exemplar based clustering"

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)