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: "sparse modeling of intrinsic correspondences"
  Abstract: We present a novel sparse modeling approach to non-rigid shape matching using only the ability to detect repeatable regions. As the input to our algorithm, we are given only two sets of regions in two shapes; no descriptors are provided so the correspondence between the regions is not know, nor we know how many regions correspond in the two shapes. We show that even with such scarce information, it is possible to establish very accurate correspondence between the shapes by using methods from the field of sparse modeling, being this, the first non-trivial use of sparse models in shape correspondence. We formulate the problem of permuted sparse coding, in which we solve simultaneously for an unknown permutation ordering the regions on two shapes and for an unknown correspondence in functional representation. We also propose a robust variant capable of handling incomplete matches. Numerically, the problem is solved efficiently by alternating the solution of a linear assignment and a sparse coding problem. The proposed methods are evaluated qualitatively and quantitatively on standard benchmarks containing both synthetic and scanned objects.

Connected through hyperedge HE-1 (induced by source paper "graph domain adaptation with localized graph signal representations"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "deep visual domain adaptation a survey"
    - "unsupervised domain adaptation with copula models"
    - "adversarial discriminative domain adaptation"
    - "geometric deep learning going beyond euclidean data"
    - "joint geometrical and statistical alignment for visual domain adaptation"
    - "deep transfer learning with joint adaptation networks"
    - "learning an invariant hilbert space for domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "deep functional dictionaries learning consistent semantic structures on 3d models from functions"):
  with members:
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "what makes for effective detection proposals"
    - "action recognition in video using sparse coding and relative features"

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"
    - "efficient globally optimal 2d to 3d deformable shape matching"
    - "geodesic distance descriptors"

Connected through hyperedge HE-4 (induced by source paper "a simple approach to intrinsic correspondence learning on unstructured 3d meshes"):
  with members:
    - "deep functional maps structured prediction for dense shape correspondence"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "farm functional automatic registration method for 3d human bodies"):
  with members:
    - "shape retrieval of non rigid 3d human models"
    - "multilevel active registration for kinect human body scans from low quality to high quality"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "detailed accurate human shape estimation from clothed 3d scan sequences"
    - "building statistical shape spaces for 3d human modeling"
    - "deformable shape completion with graph convolutional autoencoders"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "localized manifold harmonics for spectral shape analysis"

Connected through hyperedge HE-6 (induced by source paper "4d human body correspondences from panoramic depth maps"):
  with members:
    - "robust 3d human motion reconstruction via dynamic template construction"
    - "stacked hourglass networks for human pose estimation"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "dense human body correspondences using convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "deep functional maps structured prediction for dense shape correspondence"):
  with members:
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "geometric deep learning going beyond euclidean data"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "dense human body correspondences using convolutional networks"
    - "deep residual learning for image recognition"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-8 (induced by source paper "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"):
  with members:
    - "efficient globally optimal 2d to 3d deformable shape matching"
    - "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"
    - "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.GR(Graphics)