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 combinatorial solution to non rigid 3d shape to image matching"
  Abstract: We propose a combinatorial solution for the problem of non-rigidly matching a 3D shape to 3D image data. To this end, we model the shape as a triangular mesh and allow each triangle of this mesh to be rigidly transformed to achieve a suitable matching to the image. By penalising the distance and the relative rotation between neighbouring triangles our matching compromises between image and shape information. In this paper, we resolve two major challenges: Firstly, we address the resulting large and NP-hard combinatorial problem with a suitable graph-theoretic approach. Secondly, we propose an efficient discretisation of the unbounded 6-dimensional Lie group SE(3). To our knowledge this is the first combinatorial formulation for non-rigid 3D shape-to-image matching. In contrast to existing local (gradient descent) optimisation methods, we obtain solutions that do not require a good initialisation and that are within a bound of the optimal solution. We evaluate the proposed method on the two problems of non-rigid 3D shape-to-shape and non-rigid 3D shape-to-image registration and demonstrate that it provides promising results.

Connected through hyperedge HE-1 (induced by source paper "mina convex mixed integer programming for non rigid shape alignment"):
  with members:
    - "zoomout spectral upsampling for efficient shape correspondence"
    - "continuous and orientation preserving correspondences via functional maps"
    - "unsupervised deep learning for structured shape matching"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "ds a flexible scalable and provably tight relaxation for matching problems"
    - "efficient globally optimal 2d to 3d deformable shape matching"
    - "alternating direction graph matching"

Connected through hyperedge HE-2 (induced by source paper "3dregnet a deep neural network for 3d point registration"):
  with members:
    - "all graphs lead to rome learning geometric and cycle consistent representations with graph convolutional networks"
    - "learning transformation synchronization"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "regnet learning the optimization of direct image to image pose registration"
    - "deepmapping unsupervised map estimation from multiple point clouds"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "eigendecomposition free training of deep networks with zero eigenvalue based losses"
    - "automatic cnn based detection of cardiac mr motion artefacts using k space data augmentation and curriculum learning"

Connected through hyperedge HE-3 (induced by source paper "minimal solvers for mini loop closures in 3d multi scan alignment"):
  with members:
    - "a minimal closed form solution for multi perspective pose estimation using points and lines"
    - "floornet a unified framework for floorplan reconstruction from 3d scans"
    - "tangent convolutions for dense prediction in 3d"
    - "learning compact geometric features"
    - "semantic visual localization"
    - "multi view deep learning for consistent semantic mapping with rgb d cameras"
    - "fine to coarse global registration of rgb d scans"
    - "point set registration coherent point drift"

Connected through hyperedge HE-4 (induced by source paper "model free consensus maximization for non rigid shapes"):
  with members:
    - "robust fitting in computer vision easy or hard"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "building statistical shape spaces for 3d human modeling"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "volumedeform real time volumetric non rigid reconstruction"
    - "mlpnp a real time maximum likelihood solution to the perspective n point problem"
    - "template based monocular 3d shape recovery using laplacian meshes"
    - "spectral generalized multi dimensional scaling"

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)