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 and orientation preserving correspondences via functional maps"
  Abstract: We propose a method for efficiently computing orientation-preserving and approximately continuous correspondences between non-rigid shapes, using the functional maps framework. We first show how orientation preservation can be formulated directly in the functional (spectral) domain without using landmark or region correspondences and without relying on external symmetry information. This allows us to obtain functional maps that promote orientation preservation, even when using descriptors, that are invariant to orientation changes. We then show how higher quality, approximately continuous and bijective pointwise correspondences can be obtained from initial functional maps by introducing a novel refinement technique that aims to simultaneously improve the maps both in the spectral and spatial domains. This leads to a general pipeline for computing correspondences between shapes that results in high-quality maps, while admitting an efficient optimization scheme. We show through extensive evaluation that our approach improves upon state-of-the-art results on challenging isometric and non-isometric correspondence benchmarks according to both measures of continuity and coverage as well as producing semantically meaningful correspondences as measured by the distance to ground truth maps.

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"
    - "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"
    - "efficient globally optimal 2d to 3d deformable shape matching"
    - "alternating direction graph matching"

Connected through hyperedge HE-2 (induced by source paper "unsupervised cycle consistent deformation for shape matching"):
  with members:
    - "shape2motion joint analysis of motion parts and attributes from 3d shapes"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "supervised fitting of geometric primitives to 3d point clouds"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "open3d a modern library for 3d data processing"
    - "3d coded 3d correspondences by deep deformation"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-3 (induced by source paper "smooth shells multi scale shape registration with functional maps"):
  with members:
    - "lbs autoencoder self supervised fitting of articulated meshes to point clouds"
    - "zoomout spectral upsampling for efficient shape correspondence"
    - "divergence free shape interpolation and correspondence"
    - "3d coded 3d correspondences by deep deformation"
    - "farm functional automatic registration method for 3d human bodies"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "efficient deformable shape correspondence via kernel matching"
    - "point wise map recovery and refinement from functional correspondence"

Connected through hyperedge HE-4 (induced by source paper "zoomout spectral upsampling for efficient shape correspondence"):
  with members:
    - "unsupervised deep learning for structured shape matching"
    - "fast and accurate intrinsic symmetry detection"
    - "farm functional automatic registration method for 3d human bodies"
    - "reversible harmonic maps between discrete surfaces"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "localized manifold harmonics for spectral shape analysis"
    - "point wise map recovery and refinement from functional correspondence"
    - "spectral generalized multi dimensional scaling"

Connected through hyperedge HE-5 (induced by source paper "shape analysis via functional map construction and bases pursuit"):
  with members:
    - "robust structure based shape correspondence"
    - "point wise map recovery and refinement from functional correspondence"
    - "functional correspondence by matrix completion"
    - "coupled quasi harmonic bases"

Connected through hyperedge HE-6 (induced by source paper "a genetic algorithm for fully automatic non isometric shape matching"):
  with members:
    - "reversible harmonic maps between discrete surfaces"
    - "ds a flexible scalable and provably tight relaxation for matching problems"

Connected through hyperedge HE-7 (induced by source paper "unsupervised deep learning for structured shape matching"):
  with members:
    - "deep functional maps structured prediction for dense shape correspondence"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "partial functional correspondence"
    - "dense human body correspondences using convolutional networks"

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.GR(Graphics)