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: "efficient globally optimal 2d to 3d deformable shape matching"
  Abstract: We propose the first algorithm for non-rigid 2D-to-3D shape matching, where the input is a 2D shape represented as a planar curve and a 3D shape represented as a surface; the output is a continuous curve on the surface. We cast the problem as finding the shortest circular path on the prod- uct 3-manifold of the surface and the curve. We prove that the optimal matching can be computed in polynomial time with a (worst-case) complexity of $O(mn^2\log(n))$, where $m$ and $n$ denote the number of vertices on the template curve and the 3D shape respectively. We also demonstrate that in practice the runtime is essentially linear in $m\!\cdot\! n$ making it an efficient method for shape analysis and shape retrieval. Quantitative evaluation confirms that the method provides excellent results for sketch-based deformable 3D shape re- trieval.

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"
    - "a combinatorial solution to non rigid 3d shape to image matching"
    - "alternating direction graph matching"

Connected through hyperedge HE-2 (induced by source paper "pix3d dataset and methods for single image 3d shape modeling"):
  with members:
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "hierarchical surface prediction for 3d object reconstruction"
    - "weakly supervised 3d reconstruction with adversarial constraint"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "learning 3d object categories by looking around them"
    - "large scale 3d shape reconstruction and segmentation from shapenet core55"
    - "semantic scene completion from a single depth image"

Connected through hyperedge HE-3 (induced by source paper "functional maps representation on product manifolds"):
  with members:
    - "deep functional maps structured prediction for dense shape correspondence"
    - "composition operators matrix representation and the finite section method a theoretical framework for maps between shapes"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "geometric deep learning going beyond euclidean data"
    - "localized manifold harmonics for spectral shape analysis"
    - "hamiltonian operator for spectral shape analysis"
    - "geodesic distance descriptors"
    - "functional correspondence by matrix completion"

Connected through hyperedge HE-4 (induced by source paper "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"):
  with members:
    - "spectrometer amortized sublinear spectral approximation of distance on graphs"
    - "point wise map recovery and refinement from functional correspondence"
    - "on the optimality of shape and data representation in the spectral domain"
    - "functional correspondence by matrix completion"
    - "matching lbo eigenspace of non rigid shapes via high order statistics"
    - "spectral generalized multi dimensional scaling"
    - "sparse modeling of intrinsic correspondences"
    - "point set registration coherent point drift"

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"
    - "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"

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)